Appian ACD300 Exam Dumps Questions and Answers

The three required steps that will allow you to connect both systems are:

• A. Create a Web API object and set up the correct security. This will allow you to define an endpoint in Appian that can receive requests from Jira via webhook. You will also need to configure the security settings for the Web API object, such as authentication method, allowed origins, and access control.
• B. Configure the connection in Jira specifying the URL and credentials. This will allow you to set up a webhook in Jira that can send requests to Appian when specific tickets change. You will need to specify the URL of the Web API object in Appian, as well as any credentials required for authentication.
• C. Create a new API Key and associate a service account. This will allow you to generate a unique token that can be used for authentication between Jira and Appian. You will also need to create a service account in Appian that has permissions to access or update data related to Jira tickets.

The other options are incorrect for the following reasons:

• D. Give the service account system administrator privileges. This is not required and could pose a security risk, as giving system administrator privileges to a service account could allow it to perform actions that are not related to Jira tickets, such as modifying system settings or accessing sensitive data.
• E. Create an integration object from Appian to Jira to periodically check the ticket status. This is not required and could cause unnecessary overhead, as creating an integration object from Appian to Jira would involve polling Jira for ticket status changes, which could consume more resources than using webhook notifications. Verified References: Appian Documentation, section “Web API” and “API Keys”.

Requirement: Archive processes 2 days after completion or cancellation. Correct match: A. Processes that need to be available for 2 days after completion or cancellation, after which are no longer required nor accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called “Archive after 2 days” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be archived and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting.

Requirement: Use system default (currently auto-archive processes 7 days after completion or cancellation). Correct match: C. Processes that remain available for 7 days after completion or cancellation, after which are archived when accessed Exact explanation of correct match taken from Appian Documentation: This strategy is called “Use system default” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 7 days, after which they will be archived when accessed. This strategy can help balance the availability and performance of process data, as it allows processes to be archived on demand rather than on a fixed schedule.

Requirement: Delete processes 2 days after completion or cancellation. Correct match: B. Processes that need to be available for 2 days after completion or cancellation, after which remain accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called “Delete after 2 days” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be deleted and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting, but it also means that process data will be permanently lost.

Requirement: Do not automatically clean-up processes. Correct match: D. Processes that need to remain available without the need to unarchive Exact explanation of correct match taken from Appian Documentation: This strategy is called “Do not automatically clean-up” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available indefinitely without being archived or deleted. This strategy can help ensure the availability and integrity of process data, but it also means that the process database will grow over time and affect the performance of process reporting.

When designing a data volume testing strategy for an Appian application that queries and writes to MySQL database, you should consider two key considerations:

• Testing with the correct amount of data should be in the definition of done as part of each sprint. Data volume testing is a type of testing that verifies how well an application performs when handling large amounts of data. Data volume testing is important to ensure that the application meets the performance and quality requirements of the users and stakeholders. By including data volume testing in the definition of done as part of each sprint, you can ensure that each feature or functionality of your application is tested with realistic data volumes before being delivered to production. This way, you can identify and resolve any potential issues or bottlenecks early in the development cycle, and avoid any surprises or delays later on.
• Data model changes must wait until towards the end of the project. Data model changes are changes that affect the structure or schema of your database, such as adding, modifying, or deleting tables, columns, indexes, or constraints. Data model changes are risky and costly to make, especially when dealing with large amounts of data. Data model changes can affect the performance, functionality, or integrity of your application and database. Therefore, data model changes must wait until towards the end of the project, when you have finalized your requirements and design decisions, and have minimized your data volume testing efforts. By waiting until towards the end of the project to make data model changes, you can reduce the impact and complexity of those changes, and avoid any unnecessary rework or regression.

The other options are not as effective. Option A, data from previous tests needs to remain in the testing environment prior to loading prepopulated data, is not a key consideration for designing a data volume testing strategy, but rather a best practice for preparing your testing environment. Option B, large datasets must be loaded via Appian processes, is not a key consideration for designing a data volume testing strategy, but rather a technical implementation detail that may or may not be suitable for your application. Option C, the amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation, is not a key consideration for designing a data volume testing strategy, but rather an input or assumption that you need to validate before conducting your data volume testing.