SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Questions and Answers

using AWS ECS on AWS Fargate since they requirements are for scalability and availability without having to provision and manage the underlying infrastructure to run the containerized workload. https://docs.aws.amazon.com/AmazonECS/latest/userguide/what-is-fargate.html

https://aws.amazon.com/blogs/aws/amazon-aurora-fast-database-cloning/

This solution meets the requirements because it uses the following components and features:

AWS Transfer Family SFTP internal server: This allows the application to securely transfer order files from the on-premises ERP system to AWS using the SFTP protocol over a private connection. The internal server is deployed in two Availability Zones for high availability and fault tolerance.

Amazon S3 storage: This provides scalable, durable, and cost-effective object storage for the order files. Amazon S3 also supports encryption at rest and in transit, as well as lifecycle policies and versioning for data protection and compliance.

AWS Lambda function: This enables the application to process the order files in a serverless manner, without provisioning or managing servers. The Lambda function can perform any custom logic or transformation on the order files, such as validating, parsing, or enriching the data.

Transfer Family managed workflow: This simplifies the orchestration of the file processing tasks by triggering the Lambda function as soon as a file is uploaded to the SFTP server. The managed workflow also provides error handling, retry policies, and logging capabilities.

This answer is correct because it meets the requirements of releasing the new version of APIs with minimal effects on customers and minimal data loss. A canary release deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage. In a canary release deployment, total API traffic is separated at random into a production release and a canary release with a pre-configured ratio. Typically, the canary release receives a small percentage of API traffic and the production release takes up the rest. The updated API features are only visible to API traffic through the canary. You can adjust the canary traffic percentage to optimize test coverage or performance. By keeping canary traffic small and the selection random, most users are not adversely affected at any time by potential bugs in the new version, and no single user is adversely affected all the time. After the test metrics pass your requirements, you can promote the canary release to the production release and disable the canary from the deployment. This makes the new features available in the production stage.

References:

https://docs.aws.amazon.com/apigateway/latest/developerguide/canary-release.html

AWS Elastic Beanstalk provides an easy and quick way to deploy, manage, and scale applications. It supports a variety of platforms, including Java and Apache Tomcat. By using Elastic Beanstalk, the solutions architect can upload the Java application and configure the environment to run Apache Tomcat.

https://aws.amazon.com/rds/aurora/global-database/

https://aws.amazon.com/blogs/architecture/using-amazon-aurora-global-database-for-low-latency-without-application-changes/

This solution satisfies the requirements in the following ways:

• AWS Backup will automatically take full backups of the DynamoDB table on the schedule defined in the backup plan (the first of each month).

• The lifecycle policy can transition backups to cold storage after 6 months, meeting that requirement.

• Setting a 7-year retention period in the backup plan will ensure each backup is retained for 7 years as required.

• AWS Backup manages the backup jobs and lifecycle policies, requiring no custom scripting or management.

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps users quickly, easily, and securely move their file or object data to, from, and between AWS storage services1. Users can use AWS DataSync to transfer data between on-premises and AWS storage services. To use AWS DataSync, users need to install an AWS DataSync agent in the on-premises data center. The agent is a software appliance that connects to the source or destination storage system and handles the data transfer to or from AWS over the network2. Users also need to use AWS DataSync to create a suitable location configuration for the on-premises SFTP server. A location is a logical representation of a storage system that contains files or objects that users want to transfer using DataSync. Users can create locations for NFS shares, SMB shares, HDFS file systems, self-managed object storage, Amazon S3 buckets, Amazon EFS file systems, Amazon FSx for Windows File Server file systems, Amazon FSx for Lustre file systems, Amazon FSx for OpenZFS file systems, Amazon FSx for NetApp ONTAP file systems, and AWS Snowcone devices3.

it allows the company to implement a disaster recovery (DR) solution for the application that has a recovery time objective (RTO) of less than 4 hours and uses the fewest possible AWS resources during normal operations. By creating Amazon Machine Images (AMIs) to back up the EC2 instances and copying the AMIs to a secondary AWS Region, the company can create point-in-time snapshots of the application and store them in a different geographical location. By automating infrastructure deployment in the secondary Region by using AWS CloudFormation, the company can quickly launch a stack of resources from a template in case of a disaster. This is a cost-effective and operationally efficient way to implement a DR solution for EC2 instances. References:

Amazon Machine Images (AMI)

Copying an AMI

AWS CloudFormation

Working with Stacks

From https://docs .aws.amazon.com/kms/latest/developerguide/custom-key-store-overview.html

For most users, the default AWS KMS key store, which is protected by FIPS 140-2 validated cryptographic modules, fulfills their security requirements. There is no need to add an extra layer of maintenance responsibility or a dependency on an additional service. However, you might consider creating a custom key store if your organization has any of the following requirements: Key material cannot be stored in a shared environment. Key material must be subject to a secondary, independent audit path. The HSMs that generate and store key material must be certified at FIPS 140-2 Level 3. https://docs.aws.amazon.com/kms/latest/developerguide/custom-key-store-overview.html

https://docs.aws.amazon.com/kms/latest/developerguide/multi-region-keys-overview.html

To ensure that Amazon S3 buckets do not have unauthorized configuration changes, a solutions architect should turn on AWS Config with the appropriate rules. AWS Config is a service that allows users to audit and assess their AWS resource configurations for compliance with industry standards and internal policies. It provides a detailed view of the resources and their configurations, including information on how the resources are related to each other. By turning on AWS Config with the appropriate rules, users can identify and remediate unauthorized configuration changes to their Amazon S3 buckets.

AWS Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle.

https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html

Secrets Manager enables you to replace hardcoded credentials in your code, including passwords, with an API call to Secrets Manager to retrieve the secret programmatically. This helps ensure the secret can't be compromised by someone examining your code, because the secret no longer exists in the code. Also, you can configure Secrets Manager to automatically rotate the secret for you according to a specified schedule. This enables you to replace long-term secrets with short-term ones, significantly reducing the risk of compromise.

https://aws.amazon.com/sqs/features/

By routing incoming requests to Amazon SQS, the company can decouple the job requests from the processing instances. This allows them to scale the number of instances based on the size of the queue, providing more resources when needed. Additionally, using an Auto Scaling group based on the queue size will automatically scale the number of instances up or down depending on the workload. Updating the software to read from the queue will allow it to process the job requests in a more efficient manner, improving the performance of the system.

To ensure that the Lambda function ingests all data in the future despite occasional network connectivity issues, the following actions should be taken:

Create an Amazon Simple Queue Service (SQS) queue and subscribe it to the SNS topic. This allows for decoupling of the notification and processing, so that even if the processing Lambda function fails, the message remains in the queue for further processing later.

Modify the Lambda function to read from the SQS queue instead of directly from SNS. This decoupling allows for retries and fault tolerance and ensures that all messages are processed by the Lambda function.

The correct answer is Option D. Deploy an S3 VPC gateway endpoint into the VPC and attach an endpoint policy that allows access to the S3 buckets. By deploying an S3 VPC gateway endpoint, the application can access the S3 buckets over a private network connection within the VPC, eliminating the need for data transfer over the internet. This can help reduce data transfer fees as well as improve the performance of the application. The endpoint policy can be used to specify which S3 buckets the application has access to.

https://docs.aws.amazon.com/systems-manager/latest/userguide/setup-launch-managed-instance.html

To provide access to the SQS queue to the other company without giving up its own account permissions, a solutions architect should create an SQS access policy that provides the other company access to the SQS queue. An SQS access policy is a resource-based policy that defines who can access the queue and what actions they can perform. The policy can specify the AWS account ID of the other company as a principal, and grant permissions for actions such as sqs:ReceiveMessage, sqs:DeleteMessage, and sqs:GetQueueAttributes. This way, the other company can poll the queue using its own credentials, without needing to assume a role or use cross-account access keys. References:

Using identity-based policies (IAM policies) for Amazon SQS

Using custom policies with the Amazon SQS access policy language

AWS Fargate is a serverless compute engine that lets users run containers without having to manage servers or clusters of Amazon EC2 instances1. Users can use AWS Fargate on Amazon Elastic Container Service (Amazon ECS) to run the containerized web application with Service Auto Scaling. Amazon ECS is a fully managed container orchestration service that supports both Docker and Kubernetes2. Service Auto Scaling is a feature that allows users to adjust the desired number of tasks in an ECS service based on CloudWatch metrics, such as CPU utilization or request count3. Users can use AWS Fargate on Amazon ECS to migrate the application to AWS with minimum code changes and minimum development effort, as they only need to package their application in containers and specify the CPU and memory requirements.

Users can also use an Application Load Balancer to distribute the incoming requests. An Application Load Balancer is a load balancer that operates at the application layer and routes traffic to targets based on the content of the request. Users can register their ECS tasks as targets for an Application Load Balancer and configure listener rules to route requests to different target groups based on path or host headers. Users can use an Application Load Balancer to improve the availability and performance of their web application.

"The Object Lock legal hold operation enables you to place a legal hold on an object version. Like setting a retention period, a legal hold prevents an object version from being overwritten or deleted. However, a legal hold doesn't have an associated retention period and remains in effect until removed." https://docs.aws.amazon.com/AmazonS3/latest/userguide/batch-ops-legal-hold.html

This answer is correct because it meets the requirements of blocking the illegitimate incoming requests in a way that has a minimal impact on legitimate users. AWS WAF is a web application firewall that helps protect your web applications or APIs against common web exploits that may affect availability, compromise security, or consume excessive resources. AWS WAF gives you control over how traffic reaches your applications by enabling you to create security rules that block common attack patterns, such as SQL injection or cross-site scripting, and rules that filter out specific traffic patterns you define. You can associate AWS WAF with an ALB to protect the web application from malicious requests. You can configure a rate-limiting rule in AWS WAF to track the rate of requests for each originating IP address and block requests from an IP address that exceeds a certain limit within a five-minute period. This way, you can mitigate potential DDoS attacks and improve the performance of your website.

References:

https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-type-rate-based.html

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior across the AWS account and workloads. GuardDuty analyzes data sources such as AWS CloudTrail event logs, Amazon VPC Flow Logs, and DNS logs to identify potential threats such as compromised instances, reconnaissance, port scanning, and data exfiltration. GuardDuty can report its findings to AWS Security Hub, which is a service that provides a comprehensive view of the security posture of the AWS account and workloads. Security Hub aggregates, organizes, and prioritizes security alerts from multiple AWS services and partner solutions, and displays them on a dashboard. This solution will meet the requirements, as it enables continuous monitoring, reporting, and visualization of malicious activity in the AWS account, workloads, and access patterns to the S3 bucket.

References:

1 provides an overview of Amazon GuardDuty and its benefits.

2 explains how GuardDuty generates and reports findings based on threat detection.

3 provides an overview of AWS Security Hub and its benefits.

4 describes how Security Hub collects and displays findings from multiple sources on a dashboard

This combination of actions will provide high availability and minimum latency for global users by using AWS Global Accelerator and Application Load Balancers. AWS Global Accelerator is a networking service that helps you improve the availability, performance, and security of your internet-facing applications by using the AWS global network. It provides two global static public IPs that act as a fixed entry point to your application endpoints, such as Application Load Balancers, in multiple Regions1. Global Accelerator uses the AWS backbone network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure. It also offers TCP and UDP support, traffic encryption, and DDoS protection2. Application Load Balancers are external load balancers that distribute incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones. They support both HTTP and HTTPS (SSL/TLS) protocols, and offer advanced features such as content-based routing, health checks, and integration with other AWS services3. By creating external Application Load Balancers in front of the application in each Region, you can ensure that the application can handle varying load patterns and scale on demand. By creating an AWS Global Accelerator accelerator to route traffic to the load balancers in each Region, you can leverage the performance, security, and availability of the AWS global network to deliver the best possible user experience.

References: 1: What is AWS Global Accelerator? - AWS Global Accelerator4, Overview section2: Network Acceleration Service - AWS Global Accelerator - AWS5, Why AWS Global Accelerator? section. 3: What is an Application Load Balancer? - Elastic Load Balancing6, Overview section.

Send the outbound connection from EC2 to Network Firewall. In Network Firewall, create stateful outbound rules to allow certain domains for software patch download and deny all other domains. https://docs.aws.amazon.com/network-firewall/latest/developerguide/suricata-examples.html#suricata-example-domain-filtering

Amazon EFS provides a simple, scalable, fully managed file system that can be simultaneously accessed from multiple EC2 instances and provides built-in redundancy. It is optimized for multiple EC2 instances to access the same files, and it is designed to be highly available, durable, and secure. It can scale up to petabytes of data and can handle thousands of concurrent connections, and is a cost-effective solution for storing and accessing large amounts of data.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/PrivateContent.html

To store and access files that are 25 GB or larger across many EC2 instances and across multiple Availability Zones, Amazon Elastic File System (Amazon EFS) is a suitable solution. Amazon EFS provides a simple, scalable, elastic file system that can be mounted on multiple EC2 instances concurrently. Amazon EFS supports high availability and durability by storing data across multiple Availability Zones within a Region.

References:

What Is Amazon Elastic File System?

Using EFS with EC2

This option is the most efficient because it requests an Amazon issued public certificate from AWS Certificate Manager (ACM), which is a service that lets you easily provision, manage, and deploy public and private SSL/TLS certificates for use with AWS services and your internal connected resources1. It also requests the certificate in the us-east-1 Region, which is required for using an ACM certificate with CloudFront2. It also meets the requirement of deploying the certificate without incurring any additional costs, as ACM does not charge for certificates that are used with supported AWS services3. This solution meets the requirement of configuring its CloudFront distribution to use SSL/TLS certificates and using a different domain name for the distribution. Option A is less efficient because it requests an Amazon issued private certificate from ACM, which is a type of certificate that can be used only within your organization or virtual private cloud (VPC). However, this does not meet the requirement of configuring its CloudFront distribution to use SSL/TLS certificates, as CloudFront requires a public certificate. It also requests the certificate in the us-east-1 Region, which is correct. Option B is less efficient because it requests an Amazon issued private certificate from ACM, which is incorrect for the same reason as option A. It also requests the certificate in the us-west-1 Region, which is incorrect as CloudFront requires a certificate in the us-east-1 Region. Option D is less efficient because it requests an Amazon issued public certificate from ACM, which is correct. However, it requests the certificate in the us-west-1 Region, which is incorrect as CloudFront requires a certificate in the us-east-1 Region.

These options are the most suitable ways to design a shared storage solution for a web application that is deployed across multiple Availability Zones and requires strong consistency. Option B uses Amazon Elastic File System (Amazon EFS) as a shared file system that can be mounted on multiple EC2 instances in different Availability Zones. Amazon EFS provides high availability, durability, scalability, and performance for file-based workloads. It also supports strong consistency, which means that any changes made to the file system are immediately visible to all clients. Option E uses Amazon S3 as a shared object store that can store the web content and serve it through Amazon CloudFront, a content delivery network (CDN). Amazon S3 provides high availability, durability, scalability, and performance for object-based workloads. It also supports strong consistency for read-after-write and list operations, which means that any changes made to the objects are immediately visible to all clients. By setting the metadata for the Cache-Control header to no-cache, the web content can be prevented from being cached by the browsers or the CDN edge locations, ensuring that the latest content is always delivered to the users.

Option A is not suitable because using AWS Storage Gateway Volume Gateway as a shared storage solution for a web application is not efficient or scalable. AWS Storage Gateway Volume Gateway is a hybrid cloud storage service that provides block storage volumes that can be mounted on-premises or on EC2 instances as iSCSI devices. It is useful for migrating or backing up data to AWS, but it is not designed for serving web content or providing strong consistency. Moreover, using Volume Gateway would incur additional costs and complexity, and it would not leverage the native AWS storage services.

Option C is not suitable because creating a shared Amazon EBS volume and mounting it on multiple EC2 instances is not possible or reliable. Amazon EBS is a block storage service that provides persistent and high-performance volumes for EC2 instances. However, EBS volumes can only be attached to one EC2 instance at a time, and they are constrained to a single Availability Zone. Therefore, creating a shared EBS volume for a web application that is deployed across multiple Availability Zones is not feasible. Moreover, EBS volumes do not support strong consistency, which means that any changes made to the volume may not be immediately visible to other clients.

Option D is not suitable because using AWS DataSync to perform continuous synchronization of data between EC2 hosts in the Auto Scaling group is not efficient or scalable. AWS DataSync is a data transfer service that helps you move large amounts of data to and from AWS storage services. It is useful for migrating or archiving data, but it is not designed for serving web content or providing strong consistency. Moreover, using DataSync would incur additional costs and complexity, and it would not leverage the native AWS storage services. References:

What Is Amazon Elastic File System?

What Is Amazon Simple Storage Service?

What Is Amazon CloudFront?

What Is AWS Storage Gateway?

What Is Amazon Elastic Block Store?

What Is AWS DataSync?

Amazon Macie can also send its findings to Amazon EventBridge, which is a serverless event bus that makes it easy to connect applications using data from a variety of sources. You can create an EventBridge rule that filters the SensitiveData event type from Macie findings and sends an Amazon SNS notification to the security team. Amazon SNS is a fully managed messaging service that enables you to send messages to subscribers or other applications. References: https://docs.aws.amazon.com/macie/latest/userguide/macie-findings.html#macie-findings-eventbridge

This answer is correct because it meets the requirements of displaying a top-10 scoreboard in near-real time and offering the ability to stop and restore the game while preserving the current scores. Amazon ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications. You can use Amazon ElastiCache for Redis to set up an ElastiCache for Redis cluster to compute and cache the scores for the web application to display. You can use Redis data structures such as sorted sets and hashes to store and rank the scores of the players, and use Redis commands such as ZRANGE and ZADD to retrieve and update the scores efficiently. You can also use Redis persistence features such as snapshots and append-only files (AOF) to enable point-in-time recovery of your data, which can help you stop and restore the game while preserving the current scores.

References:

https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/WhatIs.html

https://redis.io/topics/data-types

https://redis.io/topics/persistence

Option C provides moving the website images onto an Amazon EFS file system that is mounted on every EC2 instance. Amazon EFS provides a scalable and fully managed file storage solution that can be accessed concurrently from multiple EC2 instances. This ensures that the website images can be accessed efficiently and consistently by all instances, improving performance In Option E The Auto Scaling group maintains a minimum of two instances, ensuring resilience by automatically replacing any unhealthy instances. Additionally, configuring an Amazon CloudFront distribution for the website further improves performance by caching content at edge locations closer to the end-users, reducing latency and improving content delivery. Hence combining these actions, the website's performance is improved through efficient image storage and content delivery

Kubernetes API requests within your cluster's VPC (such as node to control plane communication) use the private VPC endpoint. https://docs.aws.amazon.com/eks/latest/userguide/cluster-endpoint.html

it allows CloudFormation to access the template in the S3 bucket without granting public access or creating additional resources. A presigned URL is a URL that is signed with the access key of an IAM user or role that has permission to access the object. The presigned URL can be used by anyone who receives it, but it expires after a specified time. By creating a presigned URL for the template object and configuring the CloudFormation stack to use it, the company can grant CloudFormation access to the template based on specific user requests and follow security best practices. References:

Using Amazon S3 Presigned URLs

Using Amazon S3 Buckets

This answer is correct because it meets the requirements of optimizing cost and reducing the workload on the database. Amazon CloudFront is a content delivery network (CDN) service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users. CloudFront delivers your content through a worldwide network of data centers called edge locations. When a user requests content that you’re serving with CloudFront, the request is routed to the edge location that provides the lowest latency (time delay), so that content is delivered with the best possible performance. You can create an Amazon CloudFront distribution to host the static web contents from an Amazon S3 bucket, which is an origin that you define for CloudFront. This way, you can offload the requests for static web content from your EC2 instances to CloudFront, which can improve the performance and availability of your website, and reduce the cost of running your EC2 instances.

References:

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/Introduction.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

To store and view engineering drawings with caching support, Amazon S3 and Amazon CloudFront are suitable solutions. Amazon S3 can store any amount of data with high durability, availability, and performance. Amazon CloudFront can distribute the engineering drawings to edge locations closer to the users, which can reduce the latency and improve the user experience. Amazon CloudFront can also cache the engineering drawings at the edge locations, which can minimize the amount of time that users wait for the drawings to load.

References:

What Is Amazon S3?

What Is Amazon CloudFront?

https://docs.aws.amazon. com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html#:~:text=Don%27t%20rely%20on%20API%20keys%20as%20your%20only%20means%20of%20authentication%20and%20authorization%20for%20your%20APIs

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-api-usage-plans.html

Storage Gateway Volume Gateway (Cached Volumes): This configuration allows you to store your primary data in Amazon S3 while retaining frequently accessed data locally in a cache for low-latency access.

Low-Latency Access: Frequently accessed data is cached locally on-premises, providing low-latency access while the less frequently accessed data is stored cost-effectively in Amazon S3.

Implementation:

Deploy a Storage Gateway appliance on-premises or in a virtual environment.

Configure it as a volume gateway with cached volumes.

Create volumes and configure your applications to use these volumes.

Minimal Infrastructure Changes: This solution integrates seamlessly with existing on-premises infrastructure, requiring minimal changes and reducing dependency on on-premises storage servers.

References:

AWS Storage Gateway Volume Gateway

Volume Gateway Cached Volumes

This answer is correct because it meets the requirements of running the ML models as independent microservices that can handle irregular and unpredictable usage patterns. By directing the requests from the API into an Amazon SQS queue, the company can decouple the request processing from the model execution, and ensure that no requests are lost due to spikes in demand. By deploying the models as Amazon ECS services that read from the queue, the company can leverage containers to isolate and package each model as a microservice, and fetch the model data from S3 at startup. By enabling AWS Auto Scaling on Amazon ECS for both the cluster and copies of the service based on the queue size, the company can automatically scale up or down the number of EC2 instances in the cluster and the number of tasks in each service to match the demand and optimize performance.

References:

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html

https://docs.aws.amazon.com/AmazonECS/latest/developerguide/Welcome.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/autoscaling-ecs.html

https://docs.a ws.amazon.com/prescriptive-guidance/latest/patterns/three-aws-glue-etl-job-types-for-converting-data-to-apache-parquet.html

Windows file server is on-premise and we need something to replicate the data to the cloud, the only option we have is AWS FSx for Windows File Server. Also, since the information is confidential and sensitive, we also want to make sure that the appropriate users have access to it in a secure manner. https://docs.aws.amazon.com/fsx/ latest/WindowsGuide/what-is.html

To meet the requirements of minimizing latency for data transmission from the devices and providing rapid failover to another AWS Region, the best solution would be to use AWS Global Accelerator in combination with a Network Load Balancer (NLB) and Amazon Elastic Container Service (Amazon ECS). AWS Global Accelerator is a service that improves the availability and performance of applications by using static IP addresses (Anycast) to route traffic to optimal AWS endpoints. With Global Accelerator, you can direct traffic to multiple Regions and endpoints, and provide automatic failover to another AWS Region.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html

Reserve instances: You will have to pay for the whole term (1 year or 3years) which is not cost effective

AWS Batch is a fully managed service that enables users to run batch jobs on AWS. It can handle different types of tasks written in different languages and run them on EC2 instances. It also integrates with Amazon EventBridge (Amazon CloudWatch Events) to schedule jobs based on time or event triggers. This solution will meet the requirements of performance, scalability and low operational overhead12.

B. Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs. This solution will not meet the requirement of low operational overhead, as it involves converting the EC2 instance to a container and using AWS App Runner, which is a service that automatically builds and deploys web applications and load balances traffic2. This is not necessary for running batch jobs.

C. Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events). This solution will not meet the requirement of performance, as AWS Lambda has a limit of 15 minutes for execution time and 10 GB for memory allocation3. These limits may not be sufficient for running 1-hour tasks.

D. Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance. This solution will not meet the requirement of low operational overhead, as it involves creating and maintaining AMIs and Auto Scaling groups, which are additional resources that need to be configured and managed2.

Reference URL: https://docs.aws.amazon.com/whitepapers/latest/aws-overview/compute-services.html

https://aws.amazon.com/premiumsupport/knowledge-center/multivalue-versus-simple-policies/

AWS Cost Anomaly Detection allows you to create monitors that track the cost and usage of your AWS resources and alert you when there is an unusual spending pattern. You can create monitors based on different dimensions, such as AWS services, accounts, tags, or cost categories. You can also create alert subscriptions that notify you by email or Amazon SNS when an anomaly is detected. You can specify the threshold and frequency of the alerts, and choose to receive weekly summaries of your anomalies.

Reference URLs:

1 https://aws.amazon.com/aws-cost-management/aws-cost-anomaly-detection/

2 https://docs.aws.amazon.com/cost-management/latest/userguide/getting-started-ad.html

3 https://docs.aws.amazon.com/cost-management/latest/userguide/manage-ad.html

A. Create an Amazon API Gateway REST API. Configure the method to use the Lambda function. Enable IAM authentication on the API. This option is the most operationally efficient as it allows you to use API Gateway to handle the HTTPS endpoint and also allows you to use IAM to authenticate the calls to the microservice. API Gateway also provides many additional features such as caching, throttling, and monitoring, which can be useful for a microservice.

it allows the company to deploy an application that processes large quantities of data in parallel and prevent groups of nodes from sharing the same underlying hardware. By running the EC2 instances in a spread placement group, the company can launch a small number of instances across distinct underlying hardware to reduce correlated failures. A spread placement group ensures that each instance is isolated from each other at the rack level. References:

Placement Groups

Spread Placement Groups

 To use an event-driven programming model with AWS Lambda and reduce operational overhead, Amazon API Gateway and Amazon DynamoDB are suitable solutions. Amazon API Gateway can receive the data from the sensors and invoke AWS Lambda functions to process the data. AWS Lambda can run code without provisioning or managing servers, and scale automatically with the incoming requests. Amazon DynamoDB can store the data in a fast and flexible NoSQL database that can handle any amount of data with consistent performance.

References:

What Is Amazon API Gateway?

What Is AWS Lambda?

What Is Amazon DynamoDB?

It uses Amazon Kinesis Data Firehose which is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3. This service requires less operational overhead as compared to option A, B, and D. Additionally, it also uses Amazon API Gateway which is a fully managed service for creating, deploying, and managing APIs. These services help in reducing the operational overhead and automating the data ingestion process.

This approach will provide both high availability and scalability for the website platform. By moving the database to Amazon Aurora with a read replica in another availability zone, it will provide a failover option for the database. The use of an Application Load Balancer and an Auto Scaling group across two availability zones allows for automatic scaling of the website to meet increased user demand. Additionally, creating an AMI from the original EC2 instance allows for easy replication of the instance in case of failure.

Amazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. You can use Amazon S3 to host static content for your website, such as HTML files, images, videos, etc. Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. AWS Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS. Fargate makes it easy for you to focus on building your applications by removing the need to provision and manage servers. You can use Amazon ECS with AWS Fargate for compute power for your containerized application logic tier. Amazon RDS is a managed relational database service that makes it easy to set up, operate, and scale a relational database in the cloud. You can use a managed Amazon RDS cluster for the database tier of your application. This solution will simplify deployment and reduce operational costs for your three-tier application. References: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html https://docs.aws.amazon.com/AmazonECS/latest/developerguide/Welcome.html https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Welcome.ht ml

Generally, for building a hierarchical relationship model, a graph database such as Amazon Neptune is a better choice. In some cases, however, DynamoDB is a better choice for hierarchical data modeling because of its flexibility, security, performance, and scale. https://docs.aws.amazon.com/prescriptive-guidance/latest/dynamodb-hierarchical-data-model/introduction.html

A gateway endpoint is a gateway that is a target for a specified route in your route table, used for traffic destined to a supported AWS service1. Amazon S3 does not support gateway endpoints, only interface endpoints2. Therefore, option A is incorrect.

An interface endpoint is an elastic network interface with a private IP address that serves as an entry point for traffic destined to a supported service1. An interface endpoint can provide secure access to Amazon S3 from within the Region, but not from the on-premises location. Therefore, option C is incorrect.

AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys to protect your data3. AWS KMS does not provide a way to access data on Amazon S3 without traversing the internet. Therefore, option D is incorrect.

AWS Transit Gateway is a service that enables you to connect your Amazon Virtual Private Clouds (VPCs) and your on-premises networks to a single gateway. You can create a gateway in AWS Transit Gateway to access Amazon S3 securely from both the Region and the on-premises location using AWS Direct Connect. Therefore, option B is correct.

The number of instances in your Auto Scaling group can be driven by how long it takes to process a message and the acceptable amount of latency (queue delay). The solution is to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain.

Amazon RDS for Microsoft SQL Server is a fully managed service that offers SQL Server 2014, 2016, 2017, and 2019 editions while offloading database administration tasks such as backups, patching, and scaling. Amazon RDS supports read replicas, which are read-only copies of the primary database that can be used for reporting purposes without affecting the performance of the online application. This solution will meet the requirements with the least operational overhead, as it does not require any code changes or manual intervention.

References:

1 provides an overview of Amazon RDS for Microsoft SQL Server and its benefits.

2 explains how to create and use read replicas with Amazon RDS.

The solution that meets the requirements of high availability, resiliency, and minimal operational effort is to use AWS Transfer for SFTP and an Amazon S3 bucket for storage. This solution allows the company to securely transfer files over SFTP to Amazon S3, which is a durable and scalable object storage service. The company can then modify the application to pull the batch files from Amazon S3 to an Amazon EC2 instance for processing. The EC2 instance can be part of an Auto Scaling group with a scheduled scaling policy to run the batch operation only at night. This way, the company can save costs by scaling down the EC2 instances when they are not needed. The other solutions do not meet all the requirements because they either use Amazon EFS or Amazon EBS for storage, which are more expensive and less scalable than Amazon S3, or they do not use a scheduled scaling policy to optimize the EC2 instances usage. References :=

AWS Transfer for SFTP

Amazon S3

Amazon EC2 Auto Scaling

An IAM policy is a document that defines the permissions for an IAM identity (such as a user, group, or role). You can use IAM policies to grant permissions to existing users and groups based on department. You can create an IAM policy that grants least privilege permission, which means that you only grant the minimum permissions required for the users to perform their tasks. You can then attach the policy to the IAM groups, which will apply the policy to all the users in those groups. This solution will reduce operational costs and simplify configuration and management of permissions. References: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

To connect two VPCs in the same Region within the same AWS account, VPC peering is the most cost-effective solution. VPC peering allows direct network traffic between the VPCs without requiring a gateway, VPN connection, or AWS Transit Gateway. VPC peering also does not incur any additional charges for data transfer between the VPCs.

References:

What Is VPC Peering?

VPC Peering Pricing

it allows the company to reduce the data output costs for accessing Amazon S3 from Amazon EC2 instances in a VPC. By provisioning a VPC gateway endpoint, the company can enable private connectivity between the VPC and S3. By configuring the route table for the private subnet to use the gateway endpoint as the route for all S3 traffic, the company can avoid using a NAT gateway, which charges for data processing and data transfer. References:

VPC Endpoints for Amazon S3

VPC Endpoints Pricing

This answer is correct because it provides redundancy for the VPN connection between the Management VPC and the data center. If one customer gateway device or one VPN tunnel becomes unavailable, the traffic can still flow over the second customer gateway device and the second VPN tunnel. This way, the single point of failure in the VPN connection is mitigated.

References:

https://docs.aws.amazon.com/vpn/latest/s2svpn/vpn-redundant-connection.html

https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/VPC/vpn-tunnel-redundancy.html

it allows the application EC2 instances to access the DynamoDB tables without exposing API credentials in the template. By creating an IAM role that has the required permissions to read and write from the DynamoDB tables and adding it to the EC2 instance profile, the application instances can use temporary security credentials that are automatically rotated by AWS. This is a secure and best practice way to grant access to AWS resources from EC2 instances. References:

IAM Roles for Amazon EC2

Using Instance Profiles

AWS Global Accelerator is a networking service that improves the performance and availability of applications for global users. It uses the AWS global network to route user traffic to the optimal endpoint based on performance and health. It also provides static IP addresses that act as a fixed entry point to the applications and support both TCP and UDP protocols1. By using AWS Global Accelerator, the solution can ensure the lowest possible latency for all users.

A. Use AWS Global Accelerator to create an accelerator. Create an Application Load Balancer (ALB) behind an accelerator endpoint that uses Global Accelerator integration and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. This solution will not work, as ALB does not support UDP protocol2.

C. Create an Amazon CloudFront content delivery network (CDN) endpoint. Create a Network Load Balancer (NLB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the NLB. Update CloudFront to use the NLB as the origin. This solution will not work, as CloudFront does not support UDP protocol3.

D. Create an Amazon Cloudfront content delivery network (CDN) endpoint. Create an Application Load Balancer (ALB) behind the endpoint and listening on the TCP and UDP ports. Update the Auto Scaling group to register instances on the ALB. Update CloudFront to use the ALB as the origin. This solution will not work, as CloudFront and ALB do not support UDP protocol23.

Reference URL: https://aws.amazon.com/global-accelerator/

Creating a read replica of the primary RDS database will offload the read-only SQL queries from the primary database, which will help to improve the performance of the web application. Read replicas are exact copies of the primary database that can be used to handle read-only traffic, which will reduce the load on the primary database and improve the performance of the web application. This solution can be implemented with minimal changes to the existing web application, as the business analysts can continue to run their queries on the read replica without modifying the code.

AWS DataSync is a data transfer service that makes it easy for you to move large amounts of data online between on-premises storage and AWS storage services over the internet or AWS Direct Connect. DataSync automatically encrypts your data in transit using TLS encryption, and verifies data integrity during transfer using checksums. DataSync can transfer data up to 10 times faster than open-source tools, and reduces operational overhead by simplifying and automating tasks such as scheduling, monitoring, and resuming transfers. References: https://aws.amazon.com/datasync/

it allows the company to encrypt the Kubernetes secrets object in the EKS cluster with the least operational overhead. By enabling secrets encryption in the EKS cluster, the company can use AWS Key Management Service (AWS KMS) to generate and manage encryption keys for encrypting and decrypting secrets at rest. This is a simple and secure way to protect sensitive information in EKS clusters. References:

Encrypting Kubernetes secrets with AWS KMS

Kubernetes Secrets

https://docs.aws.amazon.com/cli/latest/reference/transfer/describe-server.html

The most efficient solution for automatically starting and stopping EC2 instances and DB instances on a schedule while minimizing cost and infrastructure maintenance is to create an AWS Lambda function and configure Amazon EventBridge to invoke the function on a schedule.

Option A, scaling EC2 instances by using elastic resize and scaling DB instances to zero outside of business hours, is not feasible as DB instances cannot be scaled to zero.

Option B, exploring AWS Marketplace for partner solutions, may be an option, but it may not be the most efficient solution and could potentially add additional costs.

Option C, launching another EC2 instance and configuring a crontab schedule to run shell scripts that will start and stop the existing EC2 instances and DB instances on a schedule, adds unnecessary infrastructure and maintenance.

To allow the MySQL database in the private subnets to access the internet without exposing it to the public, a NAT gateway is a suitable solution. A NAT gateway enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating a connection with those instances. A NAT gateway resides in the public subnets and can handle high throughput of traffic with low latency. A NAT gateway is also a managed service that does not require any operational overhead.

References:

NAT Gateways

NAT Gateway Pricing

An egress-only internet gateway is a VPC component that allows outbound communication over IPv6 from instances in your VPC to the internet, and prevents the internet from initiating an IPv6 connection with your instances. This meets the company’s security policy and requirements. To use an egress-only internet gateway, you need to add a route in the subnet’s route table that routes IPv6 internet traffic (::/0) to the egress-only internet gateway.

Reference URLs:

1 https://docs.aws.amazon.com/vpc/latest/userguide/egress-only-internet-gateway.html

2 https://dev.to/aws-builders/what-is-an-egress-only-internet-gateways-in-aws-7gp

3 https://docs.aws.amazon.com/vpc/latest/userguide/route-table-options.html

By placing the JSON documents in an S3 bucket, the documents will be stored in a highly durable and scalable object storage service. The use of AWS Lambda allows the company to run their Python code to process the documents as they arrive in the S3 bucket without having to worry about the underlying infrastructure. This also allows for horizontal scalability, as AWS Lambda will automatically scale the number of instances of the function based on the incoming rate of requests. The results can be stored in an Amazon Aurora DB cluster, which is a fully-managed, high-performance database service that is compatible with MySQL and PostgreSQL. This will provide the necessary durability and scalability for the results of the processing.

https://aws.amazon.com/rds/aurora/

This answer is correct because it meets the requirements of securely migrating the existing data to AWS and satisfying the new regulation. AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and Amazon S3. DataSync automatically encrypts data in transit and verifies data integrity during transfer. AWS CloudTrail is a service that records AWS API calls for your account and delivers log files to Amazon S3. CloudTrail can log data events, which show the resource operations performed on or within a resource in your AWS account, such as S3 object-level API activity. By using CloudTrail to log data events, you can audit access at all levels of the stored data.

References:

https://docs.aws.amazon.com/datasync/latest/userguide/what-is-datasync.html

https://docs.aws.amazon.com/awscloudtrail/latest/userguide/logging-data-events-with-cloudtrail.html

Create a t2 micro Amazon EC2 instance. Deploy a LAMP (Linux Apache MySQL, PHP/Perl/Python) stack to host the webpage. Use client-side scripting to build the contact form. Integrate the form with Amazon WorkMail. This solution will provide the company with the necessary components to host the contact form page and integrate it with Amazon WorkMail at the lowest cost. Option A requires the use of Amazon ECS, which is more expensive than EC2, and Option B requires the use of Amazon API Gateway, which is also more expensive than EC2. Option C requires the use of Amazon Lightsail, which is more expensive than EC2.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

This option is the most efficient because it uses AWS Key Management Service (AWS KMS), which is a service that makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and with your applications running on AWS1. It also uses AWS KMS to encrypt the EBS volumes and Aurora database storage at rest, which provides data protection by encrypting your data with encryption keys that you manage23. It also uses AWS Certificate Manager (ACM), which is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources. It also attaches an ACM certificate to the ALB to encrypt data in transit, which provides data protection by enabling SSL/TLS encryption for connections between clients and the load balancer. This solution meets the requirement of encrypting all data for the application at rest and in transit. Option A is less efficient because it uses AWS KMS certificates on the ALB to encrypt data in transit, which is not possible as AWS KMS does not provide certificates but only keys. It also uses AWS Certificate Manager (ACM) to encrypt the EBS volumes and Aurora database storage at rest, which is not possible as ACM does not provide encryption but only certificates. Option B is less efficient because it uses the AWS root account to log in to the AWS Management Console, which is not recommended as it has unrestricted access to all resources in your account. It also uploads the company’s encryption certificates, which is not necessary as ACM can provide certificates for free. It also selects the option to turn on encryption for all data at rest and in transit for the account, which is not possible as encryption settings are specific to each service and resource. Option D is less efficient because it uses BitLocker to encrypt all data at rest, which is a Windows feature that provides encryption for volumes on Windows servers. However, this does not provide encryption for Aurora database storage at rest, as Aurora runs on Linux servers. It also imports the company’s TLS certificate keys to AWS KMS, which is not necessary as ACM can provide certificates for free. It also attaches the KMS keys to the ALB to encrypt data in transit, which is not possible as ALB requires certificates and not keys.

The Kubernetes Cluster Autoscaler is a component that automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled onto other nodes. It uses Auto Scaling groups to scale up or down the nodes according to the demand and capacity of your cluster1.

By using the Kubernetes Cluster Autoscaler in your Amazon EKS cluster, you can achieve the following benefits:

You can improve the performance and availability of your container applications by ensuring that there are enough nodes to run your pods and that there are no idle nodes wasting resources.

You can reduce the administrative overhead of managing your cluster size manually or using custom scripts. The Cluster Autoscaler handles the scaling decisions and actions for you based on the metrics and events from your cluster.

You can leverage the integration of Amazon EKS and AWS Auto Scaling to optimize the cost and efficiency of your cluster. You can use features such as launch templates, mixed instances policies, and spot instances to customize your node configuration and save up to 90% on compute costs2

https://docs.aws.amazon.com/waf/latest/developerguide/web-acl.html

A rate-based rule in AWS WAF allows the security team to configure thresholds that trigger rate-based rules, which enable AWS WAF to track the rate of requests for a specified time period and then block them automatically when the threshold is exceeded. This provides the ability to prevent HTTP flood attacks with minimal operational overhead.

This option will scale up capacity faster in the morning to improve performance, but will still allow capacity to scale down during off hours. It achieves this as follows: • A target tracking action scales based on a CPU utilization target. By triggering at a lower CPU threshold in the morning, the Auto Scaling group will start scaling up sooner as traffic ramps up, launching instances before utilization gets too high and impacts performance. • Decreasing the cooldown period allows Auto Scaling to scale more aggressively, launching more instances faster until the target is reached. This speeds up the ramp-up of capacity. • However, unlike a scheduled action to set a fixed minimum/maximum capacity, with target tracking the group can still scale down during off hours based on demand. This helps minimize costs.

Configure provisioned concurrency for the Lambda function that handles the requests. Provisioned concurrency allows you to set the amount of compute resources that are available to the Lambda function, so that it can handle more requests at once and reduce latency. Caching the results of the queries in Amazon S3 could also help to reduce latency, but it would not be as effective as setting up provisioned concurrency. Increasing the size of the database would not help to reduce latency, as this would not increase the number of connections the Lambda function could establish, and establishing a direct connection between the frontend application and the database would bypass the API, which would not be the best solution either.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

Amazon Aurora Serverless for MySQL is a fully managed, auto-scaling relational database service that scales up or down automatically based on the application demand. This service provides all the capabilities of Amazon Aurora, such as high availability, durability, and security, without requiring the customer to provision any database instances. With Amazon Aurora Serverless for MySQL, the sales team can enjoy minimal downtime since the database is designed to automatically scale to accommodate the increased traffic. Additionally, the service allows the customer to pay only for the capacity used, making it cost-effective for infrequent access patterns. Amazon RDS for MySQL could also be an option, but it requires the customer to select an instance type, and the database administrator would need to monitor and adjust the instance size manually to accommodate the increasing traffic.

https://aws.amazon.com/ec2/dedicated-hosts/ Amazon EC2 Dedicated Hosts allow you to use your eligible software licenses from vendors such as Microsoft and Oracle on Amazon EC2, so that you get the flexibility and cost effectiveness of using your own licenses, but with the resiliency, simplicity and elasticity of AWS.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

References:

Amazon FSx for Windows File Server

Using Microsoft Windows file shares

The allowed block size is between a /28 netmask and /16 netmask. The CIDR block must not overlap with any existing CIDR block that's associated with the VPC. https://docs.aws.amazon.com/vpc/latest/userguide/configure-your-vpc.html

https://docs.aws.amazon.com/autoscaling/application/userguide/what-is-application-auto-scaling.html

https://aws.amazon.com/fsx/lustre/

Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance, scalable storage for compute workloads. Many workloads such as machine learning, high performance computing (HPC), video rendering, and financial simulations depend on compute instances accessing the same set of data through high-performance shared storage.

This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data." https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

Lambda server-less is scalable and elastic than EC2 api gateway solution

https://aws.amazon.com/premiumsupport/knowledge-center/s3-private-connection-no-authentication/

For C: IMPROVED USER EXPERIENCE Lambda@Edge can help improve your users' experience with your websites and web applications across the world, by letting you personalize content for them without sacrificing performance. Real-time Image Transformation You can customize your users' experience by transforming images on the fly based on the user characteristics. For example, you can resize images based on the viewer's device type—mobile, desktop, or tablet. You can also cache the transformed images at CloudFront Edge locations to further improve performance when delivering images. https://aws.amazon.com/lambda/edge/

A because Elasticache, despite being ideal for leaderboards per Amazon, doesn't cache at edge locations. D because FSx has higher performance for low latency needs. https://www.techtarget.com/searchaws/tip/Amazon-FSx-vs-EFS-Compare-the-AWS-file-services "FSx is built for high performance and submillisecond latency using solid-state drive storage volumes. This design enables users to select storage capacity and latency independently. Thus, even a subterabyte file system can have 256 Mbps or higher throughput and support volumes up to 64 TB."

Amazon S3 is an object storage service that can store static files such as images, videos, documents, etc. Amazon EFS is a file storage service that can store files in a hierarchical structure and supports NFS protocol. Amazon FSx for Windows File Server is a file storage service that can store files in a hierarchical structure and supports SMB protocol. Amazon EBS is a block storage service that can store data in fixed-size blocks and attach to EC2 instances.

Based on these definitions, the combination of steps that should be taken to meet the requirements are:

A. Store the static files on Amazon S3. Use Amazon CloudFront to cache objects at the edge. D. Store the server-side code on Amazon FSx for Windows File Server. Mount the FSx for Windows File Server volume on each EC2 instance to share the files.

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

Amazon RDS Blue/Green Deployments is the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed "green" environment with the upgraded database version. You can test the new version in the green environment while the "blue" environment continues serving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

This solution offers the least operational overhead while meeting the security and global availability requirements:

Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.

AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.

RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).

Option A and D involve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.

Option C suggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.

AWS References:

Amazon S3 and CloudFront Integration

AWS Lambda with API Gateway

Amazon RDS for MySQL

https://aws.amazon.com/kinesis/data- firehose/features/?nc=sn&loc=2#:~:text=into%20Amazon%20S3%2C%20Amazon%20Redshift%2C%20Amazon%20OpenSearch%20Service%2C%20Kinesis,Delivery%20streams

The S3 Block Public Access feature allows you to restrict public access to S3 buckets and objects within the account. You can enable this feature at the account level to prevent any S3 bucket from being made public, regardless of the bucket policy settings. AWS Organizations can be used to apply a Service Control Policy (SCP) to the account to prevent IAM users from changing this setting, ensuring that all S3 objects remain private. This is a straightforward and effective solution that requires minimal operational overhead.

https://docs.aws.amazon.com/kinesisanalytics/l atest/dev/what-is.html

This answer is correct because it meets the requirements of transitioning to an event-driven architecture, using serverless concepts, and minimizing operational overhead. AWS Step Functions is a serverless service that lets you coordinate multiple AWS services into workflows using state machines. State machines are composed of tasks and transitions that define the logic and order of execution of the workflow steps. AWS Lambda is a serverless function-as-a-service platform that lets you run code without provisioning or managing servers. Lambda functions can be invoked by Step Functions as tasks in a state machine, and can perform different aspects of the data management workflow, such as data ingestion, transformation, validation, and analysis. By using Step Functions and Lambda, the company can benefit from the following advantages:

Event-driven: Step Functions can trigger Lambda functions based on events, such as timers, API calls, or other AWS service events. Lambda functions can also emit events to other services or state machines, creating an event-driven architecture.

Serverless: Step Functions and Lambda are fully managed by AWS, so the company does not need to provision or manage any servers or infrastructure. The company only pays for the resources consumed by the workflows and functions, and can scale up or down automatically based on demand.

Operational overhead: Step Functions and Lambda simplify the development and deployment of workflows and functions, as they provide built-in features such as monitoring, logging, tracing, error handling, retry logic, and security. The company can focus on the business logic and data processing rather than the operational details.

References:

What is AWS Step Functions?

What is AWS Lambda?

This option is the most efficient because it uses data filters, which are specifications that restrict access to certain data in query results and engines integrated with Lake Formation1. Data filters can be used to implement row-level security and cell-level security, which are techniques to prevent access to portions of the data that contain sensitive information2. Data filters can be applied when granting Lake Formation permissions on a Data Catalog table, and can use PartiQL expressions to filter data based on conditions3. This solution meets the requirement of providing a secure solution to prevent access to portions of the data that contain sensitive information. Option A is less efficient because it uses an IAM role that includes permissions to access Lake Formation tables, which is a way to grant access to data in Lake Formation using IAM policies4. However, this does not provide a way to prevent access to portions of the data that contain sensitive information. Option C is less efficient because it uses an AWS Lambda function that removes sensitive information before Lake Formation ingests the data, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency. Option D is less efficient because it uses an AWS Lambda function that periodically queries and removes sensitive information from Lake Formation tables, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency.

AWS Shield is a managed service that provides protection against Distributed Denial of Service (DDoS) attacks for applications running on AWS. AWS Shield Standard is automatically enabled to all AWS customers at no additional cost. AWS Shield Advanced is an optional paid service. AWS Shield Advanced provides additional protections against more sophisticated and larger attacks for your applications running on Amazon Elastic Compute Cloud (EC2), Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Route 53.

https://docs.aws.amazon.com/w af/latest/developerguide/ddos-event-mitigation-logic-gax.html

AWS Step Functions is a fully managed service that makes it easy to build applications by coordinating the components of distributed applications and microservices using visual workflows. With Step Functions, you can combine multiple AWS Lambda functions into responsive serverless applications and orchestrate data and services that run on Amazon EC2 instances, containers, or on-premises servers. Step Functions also allows for manual approvals as part of the workflow. This solution meets all the requirements with the least operational overhead.

https://aws. amazon.com/step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.AuroraMySQL.html

Move the data objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days - will meet the requirements of keeping the data immediately accessible with high availability and resiliency, while minimizing storage costs. S3 Standard-IA is designed for infrequently accessed data, and it provides a lower storage cost than S3 Standard, while still offering the same low latency, high throughput, and high durability as S3 Standard.

https://aws.amazon.com/blogs/security/how-to-prevent-uploads-of-unencrypted-objects-to- amazon-s3/#:~:text=Solution%20overview,console%2C%20CLI%2C%20or%20SDK.&text=To%20encrypt%20an%20object%20at,S3%2C%20or%20SSE%2DKMS.

https://aws.ama zon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

The solution that meets the requirements with the least operational overhead is to create a **Regional AWS WAF web ACL with a rate-based rule** and associate the web ACL with the API Gateway stage. This solution will protect the application from HTTP flood attacks by monitoring incoming requests and blocking requests from IP addresses that exceed the predefined rate. Amazon CloudFront distribution with Lambda@Edge in front of the API Gateway Regional API endpoint is also a good solution but it requires more operational overhead than the previous solution. Using Amazon CloudWatch metrics to monitor the Count metric and alerting the security team when the predefined rate is reached is not a solution that can protect against HTTP flood attacks. Creating an Amazon CloudFront distribution in front of the API Gateway Regional API endpoint with a maximum TTL of 24 hours is not a solution that can protect against HTTP flood attacks.

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

Requirement Analysis: The application involves batch processing of large data transfers between EC2 instances.

Data Transfer Costs: Data transfer within the same Availability Zone (AZ) is typically free, while cross-AZ transfers incur additional costs.

Implementation:

Launch all EC2 instances within the same Availability Zone.

Ensure the instances are part of the same subnet to facilitate seamless data transfer.

Conclusion: Placing all EC2 instances in the same AZ reduces data transfer costs significantly without affecting the application’s functionality.

References

AWS Pricing: AWS Data Transfer Pricing

https://docs.aws.amazon.com/sns/latest/dg/sns-server-side-encryption.html https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-server-side-encryption.html

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, using AWS Batch is the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

The combination of these solutions provides an efficient and automated way to migrate data while preserving metadata and ensuring cleanup:

Create a new S3 bucket with versioning enabled (Option A) to preserve object metadata like version IDs during migration.

Use S3 batch operations (Option B) to efficiently copy data from specific prefixes in the source bucket to the destination bucket, ensuring minimal overhead.

Use an S3 Lifecycle policy (Option F) to automatically delete the data from the source bucket after it has been migrated, reducing manual intervention.

Option C (CopyObject API): This approach would require more manual scripting and effort.

Option D (Same-Region Replication): SRR is designed for ongoing replication, not for one-time migrations.

Option E (DataSync): DataSync adds more complexity than necessary for this task.

AWS References:

S3 Batch Operations

S3 Lifecycle Policies

Key phrase in the Question is must scale read and write capacity. Aurora is only for Read. Amazon DynamoDB has two read/write capacity modes for processing reads and writes on your tables: On-demand Provisioned (default, free-tier eligible) https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

References:

Amazon EBS Encryption

Amazon RDS Encryption

AWS Key Management Service

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

References:

Amazon Kinesis Data Firehose

AWS Fargate

Amazon Cognito provides scalable, serverless authentication, and Lambda@Edge is used for authorization, providing low-latency access control at the edge. Amazon CloudFront serves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.

Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.

Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.

Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.

AWS References:

Amazon Cognito

Lambda@Edge

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

References:

AWS Glue

Amazon Athena

For improving availability and performance of the hybrid application, the following solutions are optimal:

AWS Global Accelerator (Option A): Global Accelerator provides high availability and improves performance by using the AWS global network to route user traffic to the nearest healthy endpoint (across AWS Regions). By adding the Network Load Balancers as endpoints, Global Accelerator ensures that traffic is routed efficiently to the closest endpoint, improving both availability and performance.

Network Load Balancer (Option D): The stateful TCP-based workload hosted on Amazon EC2 instances and the stateless UDP-based workload hosted on-premises are best served by Network Load Balancers (NLBs). NLBs are designed to handle TCP and UDP traffic with ultra-low latency and can route traffic to both EC2 and on-premises endpoints.

Option B (CloudFront and Route 53): CloudFront is better suited for HTTP/HTTPS workloads, not for TCP/UDP-based applications.

Option C (ALB): Application Load Balancers do not support the stateless UDP-based workload, making NLBs the better choice for both TCP and UDP.

AWS References:

AWS Global Accelerator

Network Load Balancer

Understanding the Requirement: The company needs to keep daily EBS snapshots for 1 month and retain monthly snapshots for 7 years due to new regulations.

Analysis of Options:

S3 Glacier Deep Archive: Moving snapshots to S3 Glacier Deep Archive involves additional complexity and might not be the most straightforward approach for EBS snapshots.

EBS Snapshots Archive: This is a cost-effective solution designed specifically for long-term storage of EBS snapshots.

Standard Tier for 7 Years: Keeping snapshots in the standard tier for 7 years is more expensive and does not optimize costs.

EBS Direct APIs to S3: This involves additional operational overhead and is not the most cost-effective approach compared to using EBS Snapshots Archive.

Best Solution:

EBS Snapshots Archive: Adding a policy to move monthly snapshots to the EBS Snapshots Archive for long-term retention is the most cost-effective and administratively simple solution.

References:

Amazon EBS Snapshots

Amazon EBS Snapshots Archive

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

References:

Amazon Aurora I/O-Optimized

Understanding the Requirement: The company needs to control the creation, rotation, and disabling of encryption keys for data stored in S3 with minimal effort.

Analysis of Options:

SSE-S3: Provides server-side encryption using S3 managed keys but does not offer full control over key management.

Customer managed key with AWS KMS (SSE-KMS): Allows the company to fully control key creation, rotation, and disabling, providing a high level of security and compliance.

AWS managed key with AWS KMS (SSE-KMS): While it provides some control, it does not offer the same level of granularity as customer-managed keys.

EC2 instance encryption and re-upload: This approach is operationally intensive and does not leverage AWS managed services for efficient key management.

Best Solution:

Customer managed key with AWS KMS (SSE-KMS): This solution meets the requirement for full control over encryption keys with minimal operational overhead, leveraging AWS managed services for secure key management.

References:

AWS Key Management Service (KMS)

Amazon S3 Encryption

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

References:

AWS Lake Formation

Tag-based access control

Understanding the Requirement: The company wants to restrict each application to its specific prefix in an S3 bucket and have granular control over the objects under each prefix.

Analysis of Options:

Dedicated S3 Access Points: Provides a scalable and flexible way to manage access to S3 buckets, allowing specific policies to be attached to each access point, thereby controlling access at the prefix level.

S3 Batch Operations: Suitable for large-scale changes but involves more operational overhead and does not dynamically control future access.

Replication to new S3 buckets: Involves unnecessary duplication of data and increased storage costs, and operational overhead for managing multiple buckets.

Combination of replication and access points: Adds unnecessary complexity and overhead compared to using access points directly.

Best Solution:

Dedicated S3 Access Points: This provides the least operational overhead while meeting the requirements for prefix-level access control and granular management.

References:

Amazon S3 Access Points

The most cost-effective solution for serving video content with different bitrates is to store multiple versions of each video in Amazon S3. S3 provides scalable and cost-effective storage for large media files. Serving the videos from a single Amazon EC2 instance ensures low-latency delivery, and S3 storage helps minimize costs.

Option A (ElastiCache): Caching large video files in memory would be prohibitively expensive and unnecessary.

Option B (Auto Scaling group): Using Auto Scaling groups to serve video is less cost-effective compared to leveraging S3 for static storage.

Option D (Kinesis Video Streams): Kinesis Video Streams is designed for real-time video streaming and is not suitable for storing and serving pre-recorded videos.

AWS References:

Amazon S3 for Media Storage

Setting the RDS backup retention policy to 30 days for automated backups through AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are not automatically managed by RDS's retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

AWS CloudTrail Lake is a fully managed service that allows the collection, storage, and querying of CloudTrail events for both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Understanding the Requirement: The company needs to map its IT infrastructure to identify and enforce security policies, with the ability to quickly query and identify security risks.

Analysis of Options:

Amazon RDS: While suitable for relational data, it is not optimized for handling complex relationships and querying those relationships, which is essential for an IT infrastructure map.

Amazon Neptune: A graph database service designed for handling highly connected data. It uses SPARQL to query graph data efficiently, making it ideal for mapping IT infrastructure and identifying relationships that pose security risks.

Amazon Redshift: A data warehouse solution optimized for complex queries on large datasets but not specifically for graph data.

Amazon DynamoDB: A NoSQL database that uses PartiQL for querying, but it is not optimized for complex relationships in graph data.

Best Option for Mapping and Querying IT Infrastructure:

Amazon Neptune provides the most suitable solution with the least operational overhead. It is purpose-built for graph data and enables efficient querying of complex relationships to identify security risks.

References:

Amazon Neptune

Querying with SPARQL

Requirement Analysis: The company wants to identify cost optimizations for EC2 instances, the Auto Scaling group, and EBS volumes with high operational efficiency.

AWS Compute Optimizer: This service provides actionable recommendations to help optimize your AWS resources, including EC2 instances, Auto Scaling groups, and EBS volumes.

Cost Recommendations: Compute Optimizer analyzes the utilization of resources and provides specific recommendations for rightsizing or optimizing the configurations.

Operational Efficiency: Using Compute Optimizer automates the process of identifying cost-saving opportunities, reducing the need for manual analysis.

Implementation:

Enable AWS Compute Optimizer for your AWS account.

Review the recommendations provided for EC2 instances, Auto Scaling groups, and EBS volumes.

Conclusion: This solution provides a comprehensive, automated approach to identifying cost optimizations with minimal operational effort.

References

AWS Compute Optimizer: AWS Compute Optimizer Documentation

The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays. Option D, using an Amazon EC2 Auto Scaling target tracking policy based on the ApproximateNumberOfMessages in the SQS queue, is the best solution.

Detailed Explanation:

Auto Scaling with Target Tracking:

Target tracking policies dynamically scale out or in based on a specific metric. For this use case, you can monitor the ApproximateNumberOfMessages in the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn’t build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiences unpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn’t resolve the issue of the processing tier’s overloaded EC2 instances.

AWS References:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

Understanding the Requirement: The order processing system requires multiple independent validation steps, each handled by separate Lambda functions, with each function accessing only the subset of order information it needs. The system should be loosely coupled to accommodate future changes.

Analysis of Options:

Amazon SQS with a new Lambda function for transformation: This involves additional complexity in creating and managing multiple SQS queues and an extra Lambda function for data transformation.

Amazon SNS with message filtering: While SNS supports message filtering, it is more suited for pub/sub messaging patterns rather than event-driven processing requiring fine-grained control over the data sent to each function.

Amazon EventBridge with input transformers: EventBridge is designed for event-driven architectures, allowing for fine-grained control with input transformers that can modify and filter the event data sent to each target Lambda function, ensuring each function receives only the necessary information.

SQS with synchronous Lambda invocations: This approach adds unnecessary complexity with synchronous invocations and is not ideal for an event-driven, loosely coupled architecture.

Best Solution:

Amazon EventBridge with input transformers: This option provides the most flexible, scalable, and loosely coupled architecture, enabling each Lambda function to receive only the required subset of data.

References:

Amazon EventBridge

EventBridge Input Transformer

To ensure high availability and scalability, the web application should run in an Auto Scaling group across two Availability Zones behind an Application Load Balancer (ALB). The database should be migrated to Amazon RDS with Multi-AZ deployment, which ensures fault tolerance and automatic failover in case of an AZ failure. This setup minimizes administrative overhead while meeting the company's requirements for high availability and scalability.

Option A: Read replicas are typically used for scaling read operations, and Multi-AZ provides better availability for a transactional database.

Option B: Replicating across AWS Regions adds unnecessary complexity for a single web application.

Option D: EC2 instances across three Availability Zones add unnecessary complexity for this scenario.

AWS References:

Auto Scaling Groups

Amazon RDS Multi-AZ

AWS Service Catalog is designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn't provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

Amazon Textract can extract text from the PDFs, and Amazon Comprehend is the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in an Amazon S3 bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

"Typically, you configure buckets to be Requester Pays buckets when you want to share data but not incur charges associated with others accessing the data. For example, you might use Requester Pays buckets when making available large datasets, such as zip code directories, reference data, geospatial information, or web crawling data." https://docs.aws.amazon.com/Am azonS3/latest/userguide/RequesterPaysBuckets.html

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS: Amazon SQS Documentation

Amazon S3 is a highly scalable, durable, and cost-effective object storage service that can store millions of images1. Amazon DynamoDB is a fully managed NoSQL database that can handle high throughput and low latency for key-value and document data2. By using S3 to store the images and DynamoDB to store the geographic codes and image S3 URLs, the solution can achieve high availability and scalability during natural disasters. It can also leverage DynamoDB’s features such as caching, auto-scaling, and global tables to improve performance and reduce costs2.

A. Store the images and geographic codes in a database table Use Oracle running on an Amazon RDS Multi-AZ DB instance. This solution will not meet the requirement of scalability and cost-effectiveness, as Oracle is a relational database that may not handle large volumes of unstructured data such as images efficiently3. It also involves higher licensing and operational costs than S3 and DynamoDB12.

C. Store the images and geographic codes in an Amazon DynamoDB table Configure DynamoDB Accelerator (DAX) during times of high load. This solution will not meet the requirement of cost-effectiveness, as storing images in DynamoDB will consume more storage space and incur higher charges than storing them in S312. It will also require additional configuration and management of DAX clusters to handle high load.

D. Store the images in Amazon S3 buckets Store geographic codes and image S3 URLs in a database table Use Oracle running on an Amazon RDS Multi-AZ DB instance. This solution will not meet the requirement of scalability and cost-effectiveness, as Oracle is a relational database that may not handle high throughput and low latency for key-value data such as geographic codes efficiently3. It also involves higher licensing and operational costs than DynamoDB2.

Reference URL: https://dynobase.dev/dynamodb-vs-s3/

AWS Secrets Manager is designed specifically to securely store and manage sensitive information such as database credentials. It integrates seamlessly with AWS services like Lambda and RDS, and it provides automatic credential rotation with minimal operational overhead.

AWS Secrets Manager: By storing the database credentials in Secrets Manager, you ensure that the credentials are securely stored, encrypted, and managed. Secrets Manager provides a built-in mechanism to automatically rotate credentials at regular intervals (e.g., every 30 days), which helps in maintaining security best practices without requiring additional manual intervention.

Lambda Integration: The Lambda function can be easily configured to retrieve the credentials from Secrets Manager using the AWS SDK, ensuring that the credentials are accessed securely at runtime.

Why Not Other Options?:

Option A (Parameter Store with Rotation): While Parameter Store can store parameters securely, Secrets Manager is more tailored for secrets management and automatic rotation, offering more features and less operational overhead.

Option C (Encrypted Lambda environment variable): Storing credentials directly in Lambda environment variables, even when encrypted, requires custom code to manage rotation, which increases operational complexity.

Option D (KMS with automatic rotation): KMS is for managing encryption keys, not for storing and rotating secrets like database credentials. This option would require more custom implementation to manage credentials securely.

AWS References:

AWS Secrets Manager - Detailed documentation on how to store, manage, and rotate secrets using AWS Secrets Manager.

Using Secrets Manager with AWS Lambda - Guidance on integrating Secrets Manager with Lambda for secure credential management.

Understanding the Requirement: The company needs to automatically detect and respond to suspicious or unexpected behavior in its AWS environment, beyond the existing AWS WAF setup.

Analysis of Options:

Amazon GuardDuty: Provides continuous monitoring and threat detection across AWS accounts and resources, including integration with AWS WAF for automated response.

AWS Firewall Manager: Manages firewall rules across multiple accounts but is more focused on central management than threat detection.

Amazon Inspector: Focuses on security assessments and vulnerability management rather than real-time threat detection.

Amazon Macie: Primarily used for data security and privacy, not comprehensive threat detection.

Best Solution:

Amazon GuardDuty with EventBridge and Lambda: This combination ensures continuous threat detection and automated response by updating AWS WAF rules based on GuardDuty findings.

References:

Amazon GuardDuty

Amazon EventBridge

AWS Lambda

EBS snapshots are point-in-time backups of EBS volumes that can be used to restore data or create new volumes. EBS snapshots can be locked to prevent accidental deletion using a feature called EBS Snapshot Lock. When a snapshot is locked, it cannot be deleted by any user, including the root user, until it is unlocked. The lock policy can also specify a retention period, after which the snapshot can be deleted. This solution will meet the requirements with the least administrative effort, as it does not require any code development or policy changes.

References:

1 explains how to lock and unlock EBS snapshots using EBS Snapshot Lock.

2 describes the concept and benefits of EBS snapshots.

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

References:

Amazon S3 Storage Lens

S3 Storage Lens Metrics

Understanding the Requirement: The university wants to centralize management and automate backups for its AWS services (EC2, RDS, and DynamoDB), reducing reliance on custom scripts.

Analysis of Options:

Third-party backup software with AWS Storage Gateway: This solution introduces external dependencies and adds complexity compared to using native AWS services.

AWS Backup: Provides a centralized, fully managed service to automate and manage backups across various AWS services, including EC2, RDS, and DynamoDB.

AWS Config: Primarily used for compliance and configuration monitoring, not for backup management.

AWS Systems Manager State Manager: Useful for configuration management but not specifically designed for managing backups.

Best Solution:

AWS Backup: This service offers the necessary functionality to centralize and automate backups, providing a streamlined and integrated solution with minimal effort.

References:

AWS Backup

Understanding the Requirement: The company wants to migrate an application to AWS, increase its availability, and use AWS WAF in the architecture.

Analysis of Options:

Auto Scaling group with ALB and WAF: This option provides high availability by distributing instances across multiple Availability Zones. The ALB ensures even traffic distribution, and AWS WAF provides security at the application layer.

Cluster placement group with ALB and WAF: Cluster placement groups are for low-latency networking within a single AZ, which does not provide the high availability across AZs.

Two EC2 instances with ALB and WAF: This setup provides some availability but does not scale automatically, missing the benefits of an Auto Scaling group.

Auto Scaling group with WAF directly: AWS WAF cannot be directly connected to an Auto Scaling group; it needs to be attached to an ALB, CloudFront distribution, or API Gateway.

Best Solution:

Auto Scaling group with ALB and WAF: This configuration ensures high availability, scalability, and security, meeting all the requirements effectively.

References:

Amazon EC2 Auto Scaling

Application Load Balancer

AWS WAF

This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3 - Information on how to store and manage data in Amazon S3.

Amazon Athena - Documentation on using Amazon Athena for querying data stored in S3 using SQL.

Understanding the Requirement: The company needs to store a copy of all new photos in the us-east-1 Region from an S3 bucket in the us-west-1 Region.

Analysis of Options:

Cross-Region Replication: Automatically replicates objects across regions with minimal operational effort once configured.

CORS Configuration: Used for allowing resources on a web page to be requested from another domain, not for replication.

S3 Lifecycle Rule: Manages the transition of objects between storage classes within the same bucket, not for cross-region replication.

S3 Event Notifications with Lambda: Requires additional configuration and management compared to Cross-Region Replication.

Best Solution:

S3 Cross-Region Replication: This solution provides an automated and efficient way to replicate objects to another region, meeting the requirement with the least operational effort.

References:

Amazon S3 Cross-Region Replication

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

References:

Amazon RDS for SQL Server

AWS Key Management Service (KMS)

Understanding the Requirement: The robotics company needs a public load balancer to ensure seamless communication with backend services, route traffic based on query strings, and encrypt traffic.

Analysis of Options:

Network Load Balancer with ACM Certificate: NLBs primarily operate at the transport layer (Layer 4) and do not natively support query parameter-based routing, which is a Layer 7 feature.

Gateway Load Balancer with IAM Certificate: Gateway Load Balancers are designed for deploying, scaling, and managing third-party virtual appliances and do not support HTTP path-based or query parameter-based routing.

Application Load Balancer with ACM Certificate: ALBs operate at the application layer (Layer 7), supporting features like query parameter-based routing and SSL/TLS termination with ACM certificates.

Network Load Balancer with IAM Certificate: As with the first option, NLBs do not support query parameter-based routing, making it unsuitable for this requirement.

Best Solution:

Application Load Balancer with ACM Certificate: This option provides the necessary Layer 7 routing capabilities and SSL/TLS termination to meet the requirements for query parameter-based routing and encrypted communication.

References:

Application Load Balancer

AWS Certificate Manager

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.

References:

AWS WAF

Using AWS WAF with AWS Global Accelerator

For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure a transient cluster (which runs for the duration of the job and then terminates) with On-Demand Instances for the primary and core nodes, and Spot Instances for the task nodes. Here's why:

Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.

Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for non-critical tasks as the framework can handle retries.

A transient cluster is more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it's not needed.

Option A: A long-running cluster may result in unnecessary costs when the cluster isn't being used.

Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.

Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

The Gateway Load Balancer (GWLB) is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts. GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).

Key AWS features:

Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.

Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.

AWS Documentation: The use of GWLB aligns with AWS’s best practices for centralized network security, simplifying architecture and reducing operational complexity​.

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

References:

IAM Roles with Cross-Account Access

Creating a Role for Cross-Account Access

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_aws-services-that-work-with-iam.html

Understanding the Requirement: The data must be encrypted at rest with automatic key rotation every year, with minimal operational overhead.

Analysis of Options:

SSE-S3: This option provides encryption with S3 managed keys and automatic key rotation but offers less control and flexibility compared to KMS keys.

AWS KMS with Customer Managed Key (automatic rotation): This option offers full control over encryption keys, with AWS KMS handling automatic key rotation, minimizing operational overhead.

AWS KMS with Customer Managed Key (manual rotation): This requires manual intervention for key rotation, increasing operational overhead.

Customer Key Material: This involves more complex management, including importing key material and setting up automatic rotation, which increases operational overhead.

Best Option for Minimal Operational Overhead:

AWS KMS with a customer managed key and automatic rotation provides the needed security and key rotation with minimal operational effort. Setting the S3 bucket’s default encryption to use this key ensures all data is encrypted as required.

References:

AWS Key Management Service (KMS)

Amazon S3 default encryption

AWS IAM Identity Center:

IAM Identity Center provides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) through SAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Center also simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuring IAM Identity Center to connect to the company's IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected, IAM Identity Center handles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separate IAM users for each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWS root users with synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D: AWS Resource Access Manager (RAM) is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn’t provide a solution for authentication via an external IdP.

AWS References:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting up IAM Identity Center and connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore, Option C is the best solution.

AWS Application Migration Service (AWS MGN) is the recommended tool for a lift-and-shift strategy, especially for time-sensitive migrations. It automates the replication of on-premises VMs to AWS, minimizing the effort required for migration and testing.

Key steps:

Replication with AWS MGN: The AWS Replication Agent is installed on the VMs to continuously replicate data to AWS, allowing you to manage migration easily.

Testing and Cutover: Initial replication allows for testing in AWS before performing the final cutover, ensuring that the migration process is smooth and data integrity is maintained.

AWS Documentation: AWS MGN is recommended for migrating virtual machines to the cloud with minimal downtime and disruption​​.

This solution meets the requirement of migrating a Windows-based application that requires the use of a shared Windows file system attached to multiple Amazon EC2 Windows instances that are deployed across multiple Availability Zones. Amazon FSx for Windows File Server provides fully managed shared storage built on Windows Server, and delivers a wide range of data access, data management, and administrative capabilities. It supports the Server Message Block (SMB) protocol and can be mounted to EC2 Windows instances across multiple Availability Zones.

Option A is incorrect because AWS Storage Gateway in volume gateway mode provides cloud-backed storage volumes that can be mounted as iSCSI devices from on-premises application servers, but it does not support SMB protocol or EC2 Windows instances. Option C is incorrect because Amazon Elastic File System (Amazon EFS) provides a scalable and elastic NFS file system for Linux-based workloads, but it does not support SMB protocol or EC2 Windows instances. Option D is incorrect because Amazon Elastic Block Store (Amazon EBS) provides persistent block storage volumes for use with EC2 instances, but it does not support SMB protocol or attaching multiple instances to the same volume.

References:

https://aws.amazon.com/fsx/windows/

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/using-file-shares.html

The details are revealed in below url: https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/FIFO-queues.html

FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can't be tolerated. Examples of situations where you might use FIFO queues include the following: To make sure that user-entered commands are run in the right order. To display the correct product price by sending price modifications in the right order. To prevent a student from enrolling in a course before registering for an account.

This solution meets the requirements of running a gaming application that transmits data by using UDP packets and scaling out and in as traffic increases and decreases. A Network Load Balancer can handle millions of requests per second while maintaining high throughput at ultra low latency, and it supports both TCP and UDP protocols. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and the scaling policies.

Option B is incorrect because an Application Load Balancer does not support UDP protocol, only HTTP and HTTPS. Option C is incorrect because Amazon Route 53 is a DNS service that can route traffic based on different policies, but it does not provide load balancing or scaling capabilities. Option D is incorrect because a NAT instance is used to enable instances in a private subnet to connect to the internet or other AWS services, but it does not provide load balancing or scaling capabilities.

References:

https://aws.amazon.com/blogs/aws/new-udp-load-balancing-for-network-load-balancer/

https://docs.aws.amazon.com/autoscaling/ec2/userguide/AutoScalingGroup.html

https://aws.amazon.com/certificate-manager/ :

"With AWS Certificate Manager, you can quickly request a certificate, deploy it on ACM-integrated AWS resources, such as Elastic Load Balancers, Amazon CloudFront distributions, and APIs on API Gateway, and let AWS Certificate Manager handle certificate renewals. It also enables you to create private certificates for your internal resources and manage the certificate lifecycle centrally."

https://aws.amazon.com/blogs/networking-and-content-delivery/deliver-your-apps-dynamic-content-using-amazon-cloudfront-getting-started-template/

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

Service control policies (SCPs) are one type of policy that you can use to manage your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization's access control guidelines. See https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.

Use a group email address for the management account's root user https://docs.aws.amazon.com/organizations/latest/userguide/orgs_best-practices_mgmt-acct.html#best-practices_mgmt-acct_email-address

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor -ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has%20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

CloudFront uses a local cache to provide the response, AWS Global accelerator proxies requests and connects to the application all the time for the response.

https://docs.aws.amazon.com/AmazonCloudFron t/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-granting-permissions-to-oai

https://aws.amazon.com/about-aws/whats-new/2017/10/aws-waf-now-supports-geographic-match/

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-web- awswaf.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ec2-placementgroup.html

"A cluster placement group is a logical grouping of instances within a single Availability Zone that benefit from low network latency, high network throughput"

1. lowest possible latency + node to node ==> cluster placement(must be within one AZ), so C, D out

2. For EBS Multi-Attach, up to 16 instances can be attached to a single volume==>we have 16 linux instance==>more close to A

3. "need a shared block device volume"==>EBS Multi-attach is Block Storage whereas EFS is File Storage==> B out

4. EFS automatically replicates data within and across 3 AZ==>we use cluster placement so all EC2 are within one AZ.

5. EBS Multi-attach volumes can be used for clients within a single AZ.

https://repost.aws/questions/QUK2RANw1QTKCwpDUwCCI72A/efs-vs-ebs-mult-attach

Composite alarms determine their states by monitoring the states of other alarms. You can **use composite alarms to reduce alarm noise**. For example, you can create a composite alarm where the underlying metric alarms go into ALARM when they meet specific conditions. You then can set up your composite alarm to go into ALARM and send you notifications when the underlying metric alarms go into ALARM by configuring the underlying metric alarms never to take actions. Currently, composite alarms can take the following actions: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Create_Composite_Alarm.html

https://docs.aws.amazon.com/autoscaling/application/userguide/application-auto-scaling-target-tracking.html

https://aws.amazon.com/dynamodb/dax/

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestration service that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service (Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

Amazon Athena is the best choice for running one-time queries on streaming data. Although Amazon Kinesis Data Analytics provides an easy and familiar standard SQL language to analyze streaming data in real-time, it is designed for continuous queries rather than one-time queries[1]. On the other hand, Amazon Athena is a serverless interactive query service that allows querying data in Amazon S3 using SQL. It is optimized for ad-hoc querying and is ideal for running one-time queries on streaming data[2].AWS Lake Formation uses as a central place to have all your data for analytics purposes (E). Athena integrate perfect with S3 and can makes queries (A).

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of protections. https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

https://docs.aws.amazon.com/AmazonRDS/latest/UserGu ide/USER_RestoreFromSnapshot.html#USER_RestoreFromSnapshot.CON

Under "Encrypt unencrypted resources" - https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSEncryptio n.html

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html https://docs.aws.amazon.com/IA M/latest/UserGuide/id_users.html

You need S3 to be able to archive the logs after one month. Cannot do that with CloudWatch Logs.

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

References:

https://aws.amazon.com/dynamodb/

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/BackupRestore.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones. Use an Amazon RDS DB instance in a Multi-AZ configuration. To make an existing application highly available and resilient while avoiding any single points of failure and giving the application the ability to scale to meet user demand, the best solution would be to deploy the application servers using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones and use an Amazon RDS DB instance in a Multi-AZ configuration. By using an Amazon RDS DB instance in a Multi-AZ configuration, the database is automatically replicated across multiple Availability Zones, ensuring that the database is highly available and can withstand the failure of a single Availability Zone. This provides fault tolerance and avoids any single points of failure.

https://docs.aws.amazon.com/AmazonRDS/late st/AuroraUserGuide/Aurora.Replication.html

#Aurora.Replication.Replicas Aurora Replicas have two main purposes. You can issue queries to them to scale the read operations for your application. You typically do so by connecting to the reader endpoint of the cluster. That way, Aurora can spread the load for read-only connections across as many Aurora Replicas as you have in the cluster. Aurora Replicas also help to increase availability. If the writer instance in a cluster becomes unavailable, Aurora automatically promotes one of the reader instances to take its place as the new writer.

https://docs.aws.amazon.com/Amazon RDS/latest/AuroraUserGuide/Aurora.Overview.html

Q: What does Amazon RDS manage on my behalf?

Amazon RDS manages the work involved in setting up a relational database: from provisioning the infrastructure capacity you request to installing the database software. Once your database is up and running, Amazon RDS automates common administrative tasks such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

https://aws.amazon.com/rds/faqs/

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/field-level-encryption.html

"With Amazon CloudFront, you can enforce secure end-to-end connections to origin servers by using HTTPS. Field-level encryption adds an additional layer of security that lets you protect specific data throughout system processing so that only certain applications can see it."

EC2 Spot Instances allow users to bid on spare Amazon EC2 computing capacity and can be a cost-effective solution for stateless, interruptible workloads that can be started and stopped at any time. Since the batch processing job is stateless, can be started and stopped at any time, and typically takes upwards of 60 minutes to complete, EC2 Spot Instances would be a good fit for this workload.

High availability can be enabled for this architecture quite simply by modifying the existing Auto Scaling group to use multiple availability zones. The ASG will automatically balance the load so you don't actually need to specify the instances per AZ.

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

https://aws.amazo n.com/premiumsupport/knowledge-center/cloudfront-access-to-amazon-s3/

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-restricting-access-to-s3-overview

https://computingforgeeks.com/stream-logs-in-aws-from-cloudwatch-to-elasticsearch/

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created. EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

References:

https://aws.amazon.com/blogs/machine-learning/automate-feature-engineering-pipelines-with-amazon-sagemaker/

https://docs.aws.amazon.com/sagemaker/latest/dg/automating-sagemaker-with-eventbridge.html

https://aws.amazon.com/about-aws/whats-new/2021/04/new-options-trigger-amazon-sagemaker-pipeline-executions/

Amazon CloudFront is a content delivery network (CDN) that speeds up the delivery of static and dynamic web content, such as HTML pages, images, and videos. By using CloudFront, the HTML pages will be served to users from the edge location that is closest to them, resulting in faster delivery and a better user experience. CloudFront can also handle the high traffic and large number of requests expected for the global event, ensuring that the HTML pages are available and accessible to users around the world.

This solution meets the requirements of saving money on storage while keeping the most accessed files readily available for the users. S3 Lifecycle policy can automatically move objects from one storage class to another based on predefined rules. S3 Standard-IA is a lower-cost storage class for data that is accessed less frequently, but requires rapid access when needed. It is suitable for ringtones older than 90 days that are downloaded infrequently.

Option A is incorrect because configuring S3 Standard-IA for the initial storage tier of the objects can incur higher costs for frequent access and retrieval fees. Option B is incorrect because moving the files to S3 Intelligent-Tiering can incur additional monitoring and automation fees that may not be necessary for ringtones older than 90 days. Option C is incorrect because using S3 inventory to manage objects and move them to S3 Standard-IA can be complex and time-consuming, and it does not provide automatic cost savings.

References:

https://aws.amazon.com/s3/storage-classes/

https://aws.amazon.com/s3/cloud-storage-cost-optimization-ebook/

AWS Fargate is a serverless experience for user applications, allowing the user to concentrate on building applications instead of configuring and managing servers. Fargate also automates resource management, allowing users to easily scale their applications in response to demand.

https://www.omnicalculator.com/other/data -transfer

For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions.

https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html

https://docs.amazonaws.cn/en_us/IAM/latest/UserGuide/reference_policies_examples_s3_rw-bucket.html

The policy is separated into two parts because the ListBucket action requires permissions on the bucket while the other actions require permissions on the objects in the bucket. You must use two different Amazon Resource Names (ARNs) to specify bucket-level and object-level permissions. The first Resource element specifies arn:aws:s3:::AdminTools for the ListBucket action so that applications can list all objects in the AdminTools bucket.

We recommend that you use On-Demand Instances for applications with short-term, irregular workloads that cannot be interrupted. https://docs.aws.amazon.co m/AWSEC2/latest/UserGuide/ec2-on-demand-instances.html

EC2 instance Savings Plan saves 72% while Compute Savings Plans saves 66%. But according to link, it says "Compute Savings Plans provide the most flexibility and help to reduce your costs by up to 66%. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, and also apply to Fargate and Lambda usage." EC2 instance Savings Plans are not applied to Fargate or Lambda

This option is the best solution because it allows the company to run its payment application on AWS with minimal operational overhead and infrastructure management. By using Amazon API Gateway, the company can create a secure and scalable API to receive payment notifications from mobile devices. By using AWS Lambda, the company can run a serverless function to validate the payment notifications and send them to the backend application. Lambda handles the provisioning, scaling, and security of the function, reducing the operational complexity and cost. By using Amazon ECS with AWS Fargate, the company can run the backend application on a fully managed container service that scales the compute resources automatically and does not require any EC2 instances to manage. Fargate allocates the right amount of CPU and memory for each container and adjusts them as needed.

A. Create an Amazon Simple Queue Service (Amazon SQS) queue Integrate the queue with an Amazon EventBndge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere Create a standalone cluster. This option is not optimal because it requires the company to manage the Kubernetes cluster that runs the backend application. Amazon EKS Anywhere is a deployment option that allows the company to create and operate Kubernetes clusters on-premises or in other environments outside AWS. The company would need to provision, configure, scale, patch, and monitor the cluster nodes, which can increase the operational overhead and complexity. Moreover, the company would need to ensure the connectivity and security between the AWS services and the EKS Anywhere cluster, which can also add challenges and risks.

B. Create an Amazon API Gateway API Integrate the API with anAWS Step Functions state ma-chine to receive payment notifications from mobile devices Invoke the state machine to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Sen/ice (Amazon EKS). Configure an EKS cluster with self-managed nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the Kubernetes cluster that runs the backend application. Amazon EKS is a fully managed service that runs Kubernetes on AWS, but it still requires the company to manage the worker nodes that run the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using AWS Step Functions to validate the payment notifications may be unnecessary and complex, as the validation logic can be implemented in a simpler way with Lambda or other services.

C. Create an Amazon Simple Queue Sen/ice (Amazon SQS) queue Integrate the queue with an Amazon EventBridge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon EC2 Spot Instances Configure a Spot Fleet with a default al-location strategy. This option is not cost-effective because it requires the company to manage the EC2 instances that run the backend application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using Spot Instances can introduce the risk of interruptions, as Spot Instances are reclaimed by AWS when the demand for On-Demand Instances increases. The company would need to handle the interruptions gracefully and ensure the availability and reliability of the backend application.

References:

1 Amazon API Gateway - Amazon Web Services

2 AWS Lambda - Amazon Web Services

3 Amazon Elastic Container Service - Amazon Web Services

4 AWS Fargate - Amazon Web Services

This option is the most efficient because it uses AWS Storage Gateway, which is a service that connects an on-premises software appliance with cloud-based storage to provide seamless integration with data security features between your on-premises IT environment and the AWS storage infrastructure1. It also uses a stored volume gateway, which is a type of volume gateway that stores your primary data locally and asynchronously backs up point-in-time snapshots of your data to Amazon S32. It also runs the Storage Gateway software application on premises and maps the gateway storage volumes to on-premises storage, which enables you to use your existing storage hardware and network infrastructure. It also mounts the gateway storage volumes to provide local access to the data, which ensures that your data is available for low latency access on premises while also getting backed up to AWS. This solution meets the requirement of maintaining local access to all the data while it is backed up on AWS and ensuring that the data backed up on AWS is automatically and securely transferred. Option A is less efficient because it uses AWS Snowball, which is a physical device that lets you transfer large amounts of data into and out of AWS3. However, this does not provide a periodic backup solution, as it requires manual handling and shipping of the device. It also configures on-premises systems to mount the Snowball S3 endpoint to provide local access to the data, which could introduce additional complexity and latency. Option B is less efficient because it uses AWS Snowball Edge, which is a physical device that has onboard storage and compute capabilities for select AWS capabilities. However, this does not provide a periodic backup solution, as it requires manual handling and shipping of the device. It also uses the Snowball Edge file interface to provide on-premises systems with local access to the data, which could introduce additional complexity and latency. Option C is less efficient because it uses AWS Storage Gateway and configures a cached volume gateway, which is a type of volume gateway that stores your primary data in Amazon S3 and retains a copy of frequently accessed data subsets locally. However, this does not provide local access to all the data, as only some data subsets are cached locally. It also configures a percentage of data to cache locally, which could incur higher costs and complexity than using a stored volume gateway.

This option is the most suitable way to improve the game’s metadata load times without migrating the database. Amazon ElastiCache for Redis is a fully managed, in-memory data store that provides sub-millisecond latency and high throughput for read-intensive workloads. You can use it as a caching layer in front of your RDS DB instance to store frequently accessed metadata and reduce the load on the database. You can also take advantage of Redis features such as snapshots, replication, and data persistence to ensure data durability and availability. ElastiCache for Redis scales automatically to meet your demand and integrates with other AWS services such as CloudFormation, CloudWatch, and IAM.

Option A is not optimal because migrating the database to Amazon Aurora with Aurora Replicas would incur additional costs and complexity. Amazon Aurora is a relational database service that provides high performance, availability, and compatibility with MySQL and PostgreSQL. Aurora Replicas are read-only copies of the primary database that can be used for scaling read capacity and enhancing availability. However, migrating the database to Aurora would require modifying the application code, testing the compatibility, and performing the data migration. Moreover, Aurora Replicas may not provide sub-millisecond response times as ElastiCache for Redis does.

Option B is not optimal because migrating the database to Amazon DynamoDB with global tables would incur additional costs and complexity. Amazon DynamoDB is a NoSQL database service that provides fast and flexible data access for any scale. Global tables are a feature of DynamoDB that enables you to replicate your data across multiple AWS Regions for high availability and performance. However, migrating the database to DynamoDB would require changing the data model, modifying the application code, and performing the data migration. Moreover, global tables may not be necessary for the game’s metadata, as they are mainly used for cross-region data access and disaster recovery.

Option D is not optimal because adding an Amazon ElastiCache for Memcached layer in front of the database would not provide the same capabilities as ElastiCache for Redis. Amazon ElastiCache for Memcached is another fully managed, in-memory data store that provides high performance and scalability for caching workloads. However, Memcached does not support snapshots, replication, or data persistence, which means that the cached data may be lost in case of a node failure or a cache eviction. Moreover, Memcached does not integrate with other AWS services as well as Redis does. Therefore, ElastiCache for Redis is a better choice for this scenario. References:

What Is Amazon ElastiCache for Redis?

What Is Amazon Aurora?

What Is Amazon DynamoDB?

What Is Amazon ElastiCache for Memcached?

A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.

A lazy loading caching strategy only loads data into the cache when it is requested, and updates the cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.

An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.

An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.

References: Caching strategies - Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache

The simplest and most effective way to ensure that all data that is written to the EBS volumes is encrypted at rest is to create the EBS volumes as encrypted volumes. You can do this by selecting the encryption option when you create a new EBS volume, or by copying an existing unencrypted volume to a new encrypted volume. You can also specify the AWS KMS key that you want to use for encryption, or use the default AWS-managed key. When you attach the encrypted EBS volumes to the EC2 instances, the data will be automatically encrypted and decrypted by the EC2 host. This solution does not require any additional IAM roles, tags, or policies.

References:

Amazon EBS encryption

Creating an encrypted EBS volume

Encrypting an unencrypted EBS volume

The most suitable design change for the company’s application is to request strongly consistent reads for the table. This change will ensure that the requests to the table return the latest data, reflecting the updates from all prior write operations.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB supports two types of read consistency: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventually consistent reads, unless users specify otherwise1.

Eventually consistent reads are reads that may not reflect the results of a recently completed write operation. The response might not include the changes because of the latency of propagating the data to all replicas. If users repeat their read request after a short time, the response should return the updated data. Eventually consistent reads are suitable for applications that do not require up-to-date data or can tolerate eventual consistency1.

Strongly consistent reads are reads that return a result that reflects all writes that received a successful response prior to the read. Users can request a strongly consistent read by setting the ConsistentRead parameter to true in their read operations, such as GetItem, Query, or Scan. Strongly consistent reads are suitable for applications that require up-to-date data or cannot tolerate eventual consistency1.

The other options are not correct because they do not address the issue of read consistency or are not relevant for the use case. Adding read replicas to the table is not correct because this option is not supported by DynamoDB. Read replicas are copies of a primary database instance that can serve read-only traffic and improve availability and performance. Read replicas are available for some relational database services, such as Amazon RDS or Amazon Aurora, but not for DynamoDB2. Using a global secondary index (GSI) is not correct because this option is not related to read consistency. A GSI is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI allows users to query the data in different ways, with eventual consistency3. Requesting eventually consistent reads for the table is not correct because this option is already the default behavior of DynamoDB and does not solve the problem of requests not returning the latest data.

References:

Read consistency - Amazon DynamoDB

Working with read replicas - Amazon Relational Database Service

Working with global secondary indexes - Amazon DynamoDB

Recycle Bin is a data recovery feature that enables you to restore accidentally deleted Amazon EBS snapshots and EBS-backed AMIs. When using Recycle Bin, if your resources are deleted, they are retained in the Recycle Bin for a time period that you specify before being permanently deleted. You can restore a resource from the Recycle Bin at any time before its retention period expires. This solution has the least operational overhead, as you do not need to create, copy, or upload any additional resources. You can also manage tags and permissions for AMIs in the Recycle Bin. AMIs in the Recycle Bin do not incur any additional charges. References:

Recover AMIs from the Recycle Bin

Recover an accidentally deleted Linux AMI

This solution meets the requirements because it requires the least amount of infrastructure management and guarantees exactly-once delivery for application messaging. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You only pay for the compute time you consume. Lambda scales automatically with the size of your workload. Amazon SQS FIFO queues are designed to ensure that messages are processed exactly once, in the exact order that they are sent. FIFO queues have high availability and deliver messages in a strict first-in, first-out order. You can use Amazon SQS to decouple and scale microservices, distributed systems, and serverless applications. References: AWS Lambda, Amazon SQS FIFO queues

This option is the best solution because it allows the company to decouple the analytics software from the user requests and scale the EC2 instances dynamically based on the demand. By using Amazon SQS, the company can create a queue that stores the user requests and acts as a buffer between the users and the analytics software. This way, the software can process the requests at its own pace without losing any data or overloading the EC2 instances. By using EC2 Auto Scaling, the company can create an Auto Scaling group that launches or terminates EC2 instances automatically based on the size of the queue. This way, the company can ensure that there are enough instances to handle the load and optimize the cost and performance of the system. By updating the software to read from the queue, the company can enable the analytics software to consume the requests from the queue and process the data from Amazon S3.

A. Create a copy of the instance Place all instances behind an Application Load Balancer. This option is not optimal because it does not address the root cause of the problem, which is the high CPU utilization of the EC2 instances. An Application Load Balancer can distribute the incoming traffic across multiple instances, but it cannot scale the instances based on the load or reduce the processing time of the analytics software. Moreover, this option can incur additional costs for the load balancer and the extra instances.

B. Create an S3 VPC endpoint for Amazon S3 Update the software to reference the endpoint. This option is not effective because it does not solve the issue of the high CPU utilization of the EC2 instances. An S3 VPC endpoint can enable the EC2 instances to access Amazon S3 without going through the internet, which can improve the network performance and security. However, it cannot reduce the processing time of the analytics software or scale the instances based on the load.

C. Stop the EC2 instances. Modify the instance type to one with a more powerful CPU and more memory. Restart the instances. This option is not scalable because it does not account for the variability of the user load. Changing the instance type to a more powerful one can improve the performance of the analytics software, but it cannot adjust the number of instances based on the demand. Moreover, this option can increase the cost of the system and cause downtime during the instance modification.

References:

1 Using Amazon SQS queues with Amazon EC2 Auto Scaling - Amazon EC2 Auto Scaling

2 Tutorial: Set up a scaled and load-balanced application - Amazon EC2 Auto Scaling

3 Amazon EC2 Auto Scaling FAQs

This answer is the most secure and recommended option for securing the root user of a new AWS account. The root user is the identity that has complete access to all AWS services and resources in the account. It is accessed by signing in with the email address and password that were used to create the account. To protect the root user credentials from unauthorized use, AWS advises the following best practices:

Create IAM users for daily administrative tasks. IAM users are identities that you create in your account that have specific permissions to access AWS resources. You can create individual IAM users for yourself and for others who need access to your account. You can also assign IAM users to IAM groups that have a set of policies that grant permissions to perform common tasks. By using IAM users instead of the root user, you can follow the principle of least privilege and reduce the risk of compromising your account.

Enable multi-factor authentication (MFA) on the root user. MFA is a security feature that requires users to prove their identity by providing two pieces of information: their password and a code from a device that only they have access to. By enabling MFA on the root user, you can add an extra layer of protection to your account and prevent unauthorized access even if your password is compromised.

Limit the tasks you perform with the root user account. You should use the root user only for tasks that require root user credentials, such as changing your account settings, closing your account, or managing consolidated billing. For a complete list of tasks that require root user credentials, see Tasks that require root user credentials. For all other tasks, you should use IAM users or roles that have the appropriate permissions.

References:

AWS account root user

Root user best practices for your AWS account

Tasks that require root user credentials

This option is the best solution because it allows the company to migrate the container application to AWS with minimal changes and leverage a managed service to run the Kubernetes cluster and the message queue. By using Amazon EKS, the company can run the container application on a fully managed Kubernetes control plane that is compatible with the existing Kubernetes tools and plugins. Amazon EKS handles the provisioning, scaling, patching, and security of the Kubernetes cluster, reducing the operational overhead and complexity. By using Amazon MQ, the company can use a fully managed message broker service that supports AMQP and other popular messaging protocols. Amazon MQ handles the administration, maintenance, and scaling of the message broker, ensuring high availability, durability, and security of the messages.

A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS) Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not optimal because it requires the company to change the container orchestration platform from Kubernetes to ECS, which can introduce additional complexity and risk. Moreover, it requires the company to change the messaging protocol from AMQP to SQS, which can also affect the application logic and performance. Amazon ECS and Amazon SQS are both fully managed services that simplify the deployment and management of containers and messages, but they may not be compatible with the existing application architecture and requirements.

C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ to retrieve the messages. This option is not ideal because it requires the company to manage the EC2 instances that host the container application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, the company would need to install and maintain the Kubernetes software on the EC2 instances, which can also add complexity and risk. Amazon MQ is a fully managed message broker service that supports AMQP and other popular messaging protocols, but it cannot compensate for the lack of a managed Kubernetes service.

D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not feasible because AWS Lambda does not support running container applications directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run container applications on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the container API, which can introduce additional complexity and overhead. Moreover, Lambda functions have limitations in terms of available CPU, memory, and runtime, which may not suit the application needs. Amazon SQS is a fully managed message queue service that supports asynchronous communication, but it does not support AMQP or other messaging protocols.

References:

1 Amazon Elastic Kubernetes Service - Amazon Web Services

2 Amazon MQ - Amazon Web Services

3 Amazon Elastic Container Service - Amazon Web Services

4 AWS Lambda FAQs - Amazon Web Services