COF-C02 SnowPro Core Certification Exam Questions and Answers

In Auto-scale mode, a Snowflake multi-cluster virtual warehouse dynamically adds or removes clusters to process queued queries efficiently.

This ensures that workloads are handled quickly without manual intervention, especially during peak demand.

References:

Snowflake Documentation: Multi-Cluster Warehouses

Snowflake Scaling Policies

The JAROWINKLER_SIMILARITY function in Snowflake returns an integer between 0 and 100, indicating the similarity of two strings based on the Jaro-Winkler similarity algorithm. This function is useful for comparing strings and determining how closely they match each other.

Understanding JAROWINKLER_SIMILARITY: The Jaro-Winkler similarity metric is a measure of similarity between two strings. The score is a number between 0 and 100, where 100 indicates an exact match and lower scores indicate less similarity.

Usage Example: To compare two strings and get their similarity score, you can use:

SELECT JAROWINKLER_SIMILARITY('string1', 'string2') AS similarity_score;

Application Scenarios: This function is particularly useful in data cleaning, matching, and deduplication tasks where you need to identify similar but not identical strings, such as names, addresses, or product titles.

In Snowflake, the CONNECT BY clause allows users to perform recursive queries. Recursive queries are used to process hierarchical or tree-structured data, such as organizational charts or file systems. The CONNECT BY clause is used in conjunction with the START WITH clause to specify the starting point of the hierarchy and the relationship between parent and child rows.

References:

Snowflake Documentation: Hierarchical Queries

The Merge operator is considered a DML (Data Manipulation Language) operator in Snowflake. DML operators are used to modify data in tables, and the Merge operator specifically allows for conditional updates, inserts, or deletes in a target table based on the results of a join with a source table.

References:

Snowflake Documentation: Query Profile Overview

Snowflake Documentation: MERGE Statement

The COPY INTO <location> command in Snowflake allows you to export or unload data from a Snowflake table into an external stage (e.g., an Amazon S3 bucket) in a specific format like JSON, CSV, or Parquet. When you want to unload data as JSON using a SELECT statement, Snowflake provides built-in functions to format the data properly into JSON structures. Among the options provided:

TO_JSON:

Converts a variant, object, or array data type into its JSON text representation.

Commonly used when exporting JSON-formatted data with COPY INTO.

This is the correct function for generating JSON-formatted output while unloading.

Example Usage:

sql

CopyEdit

COPY INTO @my_stage/unload_data.json

FROM (SELECT TO_JSON(data_column) FROM my_table)

FILE_FORMAT = (TYPE = JSON);

Why the Other Options Are Incorrect:

TO_OBJECT: This function does not exist in Snowflake.

PARSE_JSON: This is used to parse a string into a variant data type representing a JSON object but is not used for exporting or creating JSON.

OBJECT_CONSTRUCT: Creates a JSON object from key-value pairs but does not convert data into JSON format for unloading.

SnowPro References:

COPY INTO Command Documentation

TO_JSON Function

Working with JSON Data in Snowflake

Secure Data Sharing allows sharing the following objects:

Views (A): Logical objects that allow controlled data sharing without exposing the underlying tables.

Dynamic tables (E): Can be shared to provide real-time data access while maintaining security.

Other Notes:

Materialized views (B): Cannot be directly shared.

External stages (C): Cannot be shared through Secure Data Sharing.

External tables (D): Data remains outside Snowflake and cannot be shared directly.

References:

Snowflake Documentation: Secure Data Sharing

Objects Supported for Sharing

 In the Snowflake Enterprise Edition, the default Time Travel retention is set to 1 day, the maximum Time Travel retention can be set up to 90 days, and the Fail-safe retention time is 7 days3.

Snowflake's governance features vary across different editions, but the OBJECT_DEPENDENCIES view is supported by all Snowflake editions. This feature is part of Snowflake's Information Schema and is designed to help users understand the dependencies between various objects in their Snowflake environment.

The OBJECT_DEPENDENCIES view provides a way to query and analyze the relationships and dependencies among different database objects, such as tables, views, and stored procedures. This is crucial for governance, as it allows administrators and data engineers to assess the impact of changes, understand object relationships, and ensure proper management of data assets.

Object tags, masking policies, and row access policies are more advanced features that offer fine-grained data governance capabilities such as tagging objects for classification, dynamically masking sensitive data based on user roles, and controlling row-level access to data. These features may have varying levels of support across different Snowflake editions, with some features being exclusive to higher-tier editions.

In Snowflake, when dealing with semi-structured data such as a JSON document stored in a VARIANT column, the proper syntax to extract a value is to use the column name followed by the path to the specific element. Since arrays in JSON are zero-indexed, the first element is referenced with [0]. Therefore, to extract the name from the first element of the elements array, the correct syntax is Source_column:elements[0].name.References: Snowflake Documentation on Semi-Structured Data

Materialized Views are supported by both database replication and replication groups in Snowflake.

Replication ensures that these views and their definitions are available across regions or accounts as part of database or group replication.

Why Other Options Are Incorrect:

A. Pipes: Supported only in replication groups, not database replication.

B. Users: Snowflake does not replicate user objects.

C. Stages: Supported only in replication groups, not database replication.

References:

Snowflake Replication Overview

The Search Optimization Service in Snowflake enhances query performance by creating a persistent data structure that enables faster access to specific data, particularly for queries with selective filters on columns not often used in clustering. This persistent structure accelerates data retrieval without depending on clustering or caching, thereby improving response times for targeted queries.

Snowflake's micro-partitioning automatically manages table structure, but search optimization allows further enhancement for certain high-frequency, specific access patterns.

In Snowflake, Secure User-Defined Functions (UDFs) can be shared with other accounts using Snowflake's data sharing feature. This allows different Snowflake accounts to securely execute the UDFs without having direct access to the underlying data the functions operate on, ensuring privacy and security. The sharing is facilitated through shares created in Snowflake, which can contain Secure UDFs along with other database objects like tables and views.References: Snowflake Documentation on Data Sharing and Secure UDFs

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, including queries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

Role of Access History: Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.

How Access History Supports Compliance Auditing:

By providing a detailed log of access events, organizations can trace data access patterns, identify unauthorized access, and ensure that data handling complies with relevant data protection laws and regulations.

Access History can be queried to extract specific events, users, time frames, and accessed objects, making it an invaluable tool for compliance officers and auditors.

In SQL, NULL values are not equal to other NULL values. To make comparisons work in join operations, the NVL function can replace NULL with a non-NULL value.

Example:

SELECT *

FROM table1 t1

JOIN table2 t2

ON NVL(t1.col, 'default') = NVL(t2.col, 'default');

Why Other Options Are Incorrect:

B. Equal_null function: No such function exists in Snowflake SQL.

C. NULLIF function: Used to return NULL when two values are equal, not for handling NULL in joins.

D. IFNULL function: Similar to NVL but less commonly used in this context.

References:

NVL Function Documentation

To unload data from a relational table to JSON format, you can use the TO_CHAR function. This function converts a number to a character string, which can then be serialized into JSON format. While there isn't a direct function specifically named for unloading to JSON, converting the necessary fields to a string representation is a common step in preparing data for JSON serialization.

References:

Snowflake Documentation: TO_CHAR Function

The privilege required on a virtual warehouse to abort any existing executing queries is OPERATE. The OPERATE privilege on a virtual warehouse allows a user to perform operational tasks on the warehouse, including starting, stopping, and restarting the warehouse, as well as aborting running queries. This level of control is essential for managing resource utilization and ensuring that the virtual warehouse operates efficiently.

References:

Snowflake Documentation on Access Control: Access Control Privileges

Excessive spillage in the Query Profile indicates that intermediate query results are being written to remote storage (disk) instead of being processed in memory. This usually happens when:

The virtual warehouse does not have sufficient resources (e.g., memory, compute power).

Large datasets or complex operations (e.g., joins, aggregations) require more memory than available.

Solution:

Increase the size of the virtual warehouse to provide more compute and memory resources.

Why Other Options Are Incorrect:

A. Poor query optimization: This could contribute, but spillage is primarily tied to resource constraints.

B. Expired temporary tables: Temporary tables have no relation to spillage.

D. Improper CTEs: The use of CTEs does not directly cause spillage.

References:

Query Profile and Performance

Snowpipe is Snowflake’s continuous data ingestion service that loads data from cloud storage (e.g., S3, Azure Blob, Google Cloud Storage) into Snowflake as soon as the files become available in a stage.

Snowpipe uses event-driven architecture to automatically detect new files and load them with low-latency.

Why Other Options Are Incorrect:

A. COPY INTO <location> command: This command is for manual or scheduled bulk data loading, not continuous, automated loading.

B. Data replication: Used for replicating databases across regions, not for data loading.

D. Direct share: Used for sharing data between Snowflake accounts, not for loading files.

References:

Snowpipe Documentation

The command provided creates a stage in Snowflake and enables the directory table with automatic refresh:

CREATE STAGE mystage

DIRECTORY = (ENABLE = TRUE)

FILE_FORMAT = myformat;

Key Details in the Command:

DIRECTORY = (ENABLE = TRUE): Enables a directory table, which stores metadata about the files in the stage (e.g., file names, sizes, and last modified timestamps).

Automatic Refresh: When ENABLE = TRUE, the directory table automatically updates its metadata whenever new files are added to the stage or existing files are modified.

FILE_FORMAT = myformat: Specifies the file format to be used for reading or writing files in the stage.

Why Other Options Are Incorrect:

B. Metadata must be manually refreshed: Incorrect because the command enables automatic refresh.

C. Storage location must be identified: Not required in this command, as the stage itself does not define external cloud storage.

D. Directory table name must be specified: Directory tables are created and managed automatically by Snowflake when enabled; no explicit naming is needed.

References:

Snowflake Stages Documentation

Directory Tables in Snowflake

In a managed access schema, only the schema owner can manage privileges.

Object owners lose their ability to make grant decisions because access control is centrally managed at the schema level.

This is useful for enforcing strict governance policies.

References:

Snowflake Documentation: Managed Access Schemas

Snowflake Best Practices for Object Ownership

A pre-signed URL in Snowflake grants access to unstructured files (e.g., in external stages) without requiring additional authentication.

The URL includes an embedded token that specifies the expiration time and access permissions.

Key Point: Anyone who has the pre-signed URL can access the referenced file for the duration of the token’s validity.

Why Other Options Are Incorrect:

B. Only the user who generates the URL can use it: The URL is shareable and not restricted to its creator.

C. Users with roles and privileges: Pre-signed URLs bypass direct role or privilege checks.

D. Role specified in GET REST API call: This is unrelated to pre-signed URL access.

References:

Pre-Signed URLs Documentation

The VOLATILE clause is used to define functions that may return different values for different rows, even when provided with the same inputs.

This clause is suitable for non-deterministic functions like those that involve random numbers or session-specific data.

References:

Snowflake Documentation: User-Defined Functions

Snowflake SQL Functions

Directory tables store metadata about staged files, such as file names, sizes, and modification timestamps.

Characteristics of directory tables:

D: They store file-level metadata, which is useful for tracking and querying files in a stage.

E: Directory tables can either be created when defining a stage or added later using ALTER STAGE.

Why Other Options Are Incorrect:

A. Separate database objects: Directory tables are part of stages, not separate objects.

B. Only used with external stages: Directory tables work with both internal and external stages.

C. Contain binary copies of files: They store metadata, not file content.

References:

Directory Tables Documentation

Snowflake's Secure Data Sharing enables sharing of data objects (e.g., tables and secure views) with other Snowflake accounts without copying or transferring the data.

Secure views are sharable because they enforce access control and prevent unauthorized access to underlying data.

Why Other Options Are Incorrect:

B. Materialized view: Materialized views cannot be shared via Secure Data Sharing.

C. External function: External functions are not supported for Secure Data Sharing.

D. User-Defined Function (UDF): UDFs cannot be shared.

References:

Secure Data Sharing

The Query Profile in Snowflake helps identify performance issues in queries.

Common issues include:

Inefficient query pruning (B): Indicates that Snowflake scanned more micro-partitions than necessary, often due to poor clustering or lack of filtering.

Exploding joins (D): Occurs when join operations produce an unexpectedly large result set, increasing resource consumption.

Why Other Options Are Incorrect:

A. Insufficient credit quota: This is an account management issue, not a query-specific issue.

C. Excessive query pruning: This is not a problem; efficient pruning is desirable.

E. Credit usage exceeding a threshold: Query Profile does not track credit thresholds directly.

References:

Query Profile Overview

In Snowflake, the term "privilege" refers to a defined level of access to an object. Privileges are specific actions that roles can perform on securable objects in Snowflake, such as tables, views, warehouses, databases, and schemas. These privileges are granted to roles and can be further granted to users through their roles, forming the basis of Snowflake’s access control framework.References: Snowflake Documentation on Access Control Privileges

To use key pair authentication in Snowflake, you need to set the public key for the user. This allows the user to authenticate using their private key.

Generate Key Pair: Generate a public and private key pair.

Set Public Key:

ALTER USER jsmith SET RSA_PUBLIC_KEY='MIIBIjANBgkqh...';

Authentication: The user can now authenticate by signing requests with the corresponding private key.

References:

Snowflake Documentation: Key Pair Authentication & Key Rotation

Snowflake Documentation: ALTER USER

Network policies in Snowflake are used to control access to Snowflake accounts based on IP address ranges. These policies can be activated by specific roles that have the necessary privileges.

Role: ACCOUNTADMIN:

The ACCOUNTADMIN role has full administrative rights across the Snowflake account.

This role can manage all aspects of the Snowflake environment, including network policies.

Role with Global ATTACH POLICY Privilege:

Any role that has been granted the global ATTACH POLICY privilege can activate network policies.

This privilege allows the role to attach policies that control network access to the account.

References:

Snowflake Documentation: Network Policies

To use the overwrite option on insert in Snowflake, the DELETE privilege must be granted to the role. This is because overwriting data during an insert operation implicitly involves deleting the existing data before inserting the new data.

Understanding the Overwrite Option: The overwrite option (INSERT OVERWRITE) allows you to replace existing data in a table with new data. This operation is particularly useful for batch-loading scenarios where the entire dataset needs to be refreshed.

Why DELETE Privilege is Required: Since the overwrite operation involves removing existing rows in the table, the executing role must have the DELETE privilege to carry out both the deletion of old data and the insertion of new data.

Granting DELETE Privilege:

To grant the DELETE privilege to a role, an account administrator can execute the following SQL command:

sqlCopy code

GRANT DELETE ON TABLE my_table TO ROLE my_role;

When a large number of concurrent queries are queued in a virtual warehouse in Snowflake, the most effective action is to scale out using a multi-cluster warehouse. Here's why:

Understanding the Problem:

When many queries are queued, it indicates that the computational resources of the warehouse are insufficient to handle the demand.

Each virtual warehouse in Snowflake provides a fixed amount of compute resources. A single cluster in the warehouse can only process a finite number of concurrent queries efficiently.

Scale-Out with Multi-Cluster Warehouse:

Snowflake's multi-cluster warehouse feature allows a warehouse to dynamically add or remove clusters based on the current workload.

With scale-out, additional clusters are automatically spun up when the query load increases. This helps distribute the workload and reduces queuing without impacting ongoing query performance.

Once the load decreases, the additional clusters are automatically suspended, optimizing costs.

Why Not Other Options?:

A. Scale-up by resizing the warehouse: While scaling up (choosing a larger size, e.g., from X-Small to Medium) increases compute power, it may not efficiently resolve queuing when many concurrent queries need to be processed. It's better suited for improving the performance of individual queries rather than handling more queries simultaneously.

C. Disable auto-suspend on the warehouse: This ensures the warehouse remains active but does not address the queuing issue. Keeping a warehouse perpetually active can lead to unnecessary costs.

D. Enable auto-resume on the warehouse: This ensures the warehouse activates when needed but does not directly impact query concurrency.

Best Practices for Scaling with Multi-Cluster Warehouses:

Configure the minimum and maximum cluster settings to allow Snowflake to handle varying workloads efficiently.

Use auto-scaling policies like "economy" or "standard" to control cluster behavior based on cost or performance requirements.

Steps to Implement Multi-Cluster Warehouse:

Navigate to the Snowflake console.

Go to the "Warehouses" tab and select the target warehouse.

Edit the warehouse settings to set:

Minimum Clusters: The minimum number of active clusters.

Maximum Clusters: The upper limit of clusters that Snowflake can scale out to.

Auto-Resume and Auto-Suspend: Ensure these are enabled for cost efficiency.

References:

Snowflake Documentation: Scaling a Multi-Cluster Warehouse

Snowflake Documentation: Query Processing and Queuing

Snowflake Best Practices for Warehouses

Managed Access Schemas: These are a special type of schema designed for fine-grained access control in Snowflake.

Roles with Grant Authority:

ACCOUNTADMIN: The top-level administrative role can grant object privileges on all objects within the account, including managed access schemas.

SECURITYADMIN: Can grant and revoke privileges on objects within the account, including managed access schemas.

Important Note: The ORGADMIN role focuses on organization-level management, not object access control.

In Snowflake, the APPLY privilege is required to set a masking policy on a table or view column. This privilege allows a user to associate a masking policy with a specific column, thereby controlling how data in that column is masked or hidden.

Create a Masking Policy: Define a masking policy using the CREATE MASKING POLICY command.

Grant APPLY Privilege: Grant the APPLY privilege on the masking policy to the relevant roles or users.

Apply the Masking Policy: Use the ALTER TABLE or ALTER VIEW command to apply the masking policy to a column.

References:

Snowflake Documentation: Data Masking

Snowflake Documentation: CREATE MASKING POLICY

Snowflake Documentation: Privileges for Masking Policies

A network policy applied at the user level takes precedence over an account-level network policy.

This allows for more granular control of network access for individual users.

References:

Snowflake Documentation: Network Policies

Transient tables in Snowflake have a default Time Travel retention period of 1 day, which allows users to access historical data within the last 24 hours. However, transient tables do not have a Fail-safe period. Fail-safe is an additional layer of data protection that retains data beyond the Time Travel period for recovery purposes in case of extreme data loss. Since transient tables are designed for temporary or intermediate workloads with no requirement for long-term durability, they do not include a Fail-safe period by default1.

References:

Snowflake Documentation on Storage Costs for Time Travel and Fail-safe

Snowflake recommends using separate virtual warehouses for loading and querying to avoid resource contention.

This ensures that querying operations are not delayed by resource-intensive loading tasks.

References:

Snowflake Documentation: Best Practices for Data Loading

Snowflake Virtual Warehouse Management

Snowflake does not support traditional large object data types like BLOB (Binary Large Object) and CLOB (Character Large Object).

Recommended alternatives:

BINARY (C): Used for binary data like images or files.

VARCHAR (E): Used for large text-based data, such as documents or logs.

References:

Snowflake Documentation: Data Types

To return results from the results cache in Snowflake, certain conditions must be met:

Privileges: The user must have the appropriate privileges on the objects associated with the query. This ensures that only authorized users can access cached data.

Time Frame: The query must have been run within 24 hours of the previously-run query. Snowflake’s results cache is designed to store the results of queries for a short period, typically 24 hours, to improve performance for repeated queries.

Snowflake automatically rotates encryption keys when they are more than 30 days old. Active keys are retired, and new keys are created. This process is part of Snowflake’s comprehensive security measures to ensure data protection and is managed entirely by the Snowflake service without requiring user intervention.

References:

Understanding Encryption Key Management in Snowflake

The ACCOUNTADMIN role is the most powerful role in Snowflake and should be limited to a select number of users within an organization. It is responsible for account-level configurations and should not be used for day-to-day object creation or management. Granting a custom role to ACCOUNTADMIN could inadvertently give broad access to users with this role, which is not a recommended security practice.

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.

E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables

Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION

Stack Overflow discussion on cluster key selection in Snowflake

The ACCOUNTADMIN role is required for a user to create and manage accounts in Snowflake. This role has the highest level of privileges and is responsible for managing all aspects of the Snowflake account, including the ability to create and manage other user accounts1.

https://docs.snowflake.com/en/user-guide/security-access-control-considerations.html

The Snowflake storage layer is responsible for storing data in an optimized, compressed, columnar format. This includes micro-partitions, which are the fundamental storage units that contain the actual data stored in Snowflake. Additionally, persisted query results, which are the results of queries that have been materialized and stored for future use, are also kept within this layer. This design allows for efficient data retrieval and management within the Snowflake architecture1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Key Concepts & Architecture | Snowflake Documentation2

 To evaluate the details of dynamic data masking, the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used. The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Dynamic Data Masking1

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouse Performance1

In Snowflake, a Pipe is the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

Snowflake Documentation on Pipes

SnowPro® Core Certification Study Guide

https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake supports several methods for authenticating users, including federated authentication, key-pair authentication, and OAuth. Federated authentication allows users to authenticate using their organization’s identity provider. Key-pair authentication uses a public-private key pair for secure login, and OAuth is an open standard for access delegation commonly used for token-based authentication. References: Authentication policies | Snowflake Documentation, Authenticating to the server | Snowflake Documentation, External API authentication and secrets | Snowflake Documentation.

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Section 1.3 - SnowPro Core Certification Study Guide1

 When unloading data to a stage, it is recommended to define an individual file format. This ensures that the data is unloaded in a consistent and expected format, which can be crucial for downstream processing and analysis2

The default file format for the COPY command in Snowflake, when not specified, is CSV (Comma-Separated Values). This format is widely used for data exchange because it is simple, easy to read, and supported by many data analysis tools.

Snowflake’s architecture allows for flexibility in account hosting across different cloud vendors and regions. This means that within a single organization, different Snowflake accounts can be set up in various cloud environments, such as AWS, Azure, or GCP, and in different geographical regions. This allows organizations to leverage the global infrastructure of multiple cloud providers and optimize their data storage and computing needs based on regional requirements, data sovereignty laws, and other considerations.

https://docs.snowflake.com/en/user-guide/intro-regions.html

Semi-structured

JSON, Parquet

Snowflake supports unloading data in several semi-structured file formats, including Parquet and JSON. These formats allow for efficient storage and querying of semi-structured data, which can be loaded directly into Snowflake tables without requiring a predefined schema12.

https://docs.snowflake.com/en/user-guide/data-unload-prepare.html#:~:text=Supported%20File%20Formats,-The%20following%20file &text=Delimited%20(CSV%2C%20TSV%2C%20etc.)

 Materialized views in Snowflake are a feature that allows for the pre-computation and storage of query results for faster query performance. This feature is available starting from the Enterprise Edition of Snowflake. It is not available in the Standard Edition, and while it is also available in higher editions like Business Critical and Virtual Private Snowflake, the Enterprise Edition is the minimum requirement.

References:

Snowflake Documentation on CREATE MATERIALIZED VIEW1.

Snowflake Documentation on Working with Materialized Views

https://docs.snowflake.com/en/sql-reference/sql/create-materialized-view.html#:~:text=Materialized%20views%20require%20Enterprise%20Edition,upgrading%2C%20please%20contact%20Snowflake%20Support .

Provisioning time can vary based on the size of the warehouse. A 4X-Large Warehouse typically has more resources and may take longer to provision compared to a X-Small Warehouse, which has fewer resources and can generally be provisioned more quickly.References: Understanding and viewing Fail-safe | Snowflake Documentation

When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table’s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables1.

Community discussions on how source data’s ordering affects a table with a cluster key

 Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data. Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

When a cloned table is replicated to a secondary database in Snowflake, the following occurs:

C. The physical data is replicated: The actual data of the cloned table is physically replicated to the secondary database. This ensures that the secondary database has its own copy of the data, which can be used for read-only purposes or failover scenarios1.

E. Metadata pointers to cloned tables are replicated: Along with the physical data, the metadata pointers that refer to the cloned tables are also replicated. This metadata includes information about the structure of the table and any associated properties2.

It’s important to note that while the physical data and metadata are replicated, the secondary database is typically read-only and cannot be used for write operations. Additionally, while there may be additional storage costs associated with the secondary account, this is not a direct result of the replication process but rather a consequence of storing additional data.

References:

SnowPro Core Exam Prep — Answers to Snowflake’s LEVEL UP: Backup and Recovery

Snowflake SnowPro Core Certification Exam Questions Set 10

In Snowflake, Streams are the objects that track Data Manipulation Language (DML) changes made to tables, such as inserts, updates, and deletes. Streams record these changes along with metadata about each change, enabling actions to be taken using the changed data. This process is known as change data capture (CDC)2.

Snowflake supports two geospatial data types: GEOGRAPHY and GEOMETRY. The GEOGRAPHY data type is used to store geospatial data that models the Earth as a perfect sphere, which is suitable for global geospatial data. This data type follows the WGS 84 standard and is used for storing points, lines, and polygons on the Earth’s surface. The GEOMETRY data type, on the other hand, represents features in a planar (Euclidean, Cartesian) coordinate system and is typically used for local spatial reference systems. Since the question specifically asks about geospatial data, which commonly refers to Earth-related spatial data, the correct answer is GEOGRAPHY3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the 90-day period mentioned.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Account Usage Schema1

 The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

References:

Snowflake Documentation on Unloading Data

Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

 The command list @mystage; is used to view the list of files that have been uploaded to an internal stage in Snowflake. The list command displays the metadata for all files in the specified stage, which in this case is mystage. This command is particularly useful for verifying that files have been successfully unloaded from a Snowflake table to the stage and for managing the files within the stage.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

 The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Access Control Privileges1

 One of the key features of Snowflake’s architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Replication and Egress Charges1

 Virtual Warehouses in Snowflake can indeed be resized while they are suspended. Resizing a warehouse involves changing the number of compute resources (servers) allocated to it, which can be done to adjust performance and cost. When a warehouse is suspended, it is not currently running any queries, but its definition and metadata remain intact, allowing for modifications like resizing.

 Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Clustering

Fail-safe is a feature in Snowflake that provides an additional layer of data protection. After the Time Travel retention period ends, Fail-safe offers a non-configurable 7-day period during which historical data may be recoverable by Snowflake. This period is designed to protect against accidental data loss and is not intended for customer access.

References: Understanding and viewing Fail-safe | Snowflake Documentation

In Snowflake, Cloud Services costs are incurred when the Cloud Services usage exceeds 10% of the compute usage (measured in credits). Therefore, scenarios A and E would result in Cloud Services charges because the Cloud Services usage is more than 10% of the compute credits used.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s official documentation on billing and usage1

 Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake’s continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe and Virtual Warehouses1

 External stages can be altered and dropped in Snowflake. An external stage points to an external location, such as an S3 bucket, where data files are stored. Users can modify the stage’s definition or drop it entirely if it’s no longer needed. This is in contrast to table stages, which are tied to specific tables and cannot be altered or dropped independently.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Stages1

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Loading1

 The Result cache is used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.

References:

Snowflake Documentation on Query Results Cache

SnowPro® Core Certification Study Guide

 The STRIP_OUTER_ARRAY file format option in Snowflake is used when loading JSON documents that are composed of a large array containing multiple records. When this option is enabled, it removes the outer array structure, which allows each record within the array to be loaded as a separate row in the table. This is particularly useful for efficiently loading JSON data that is structured as an array of records1.

References:

Snowflake Documentation on JSON File Format

[COF-C02] SnowPro Core Certification Exam Study Guide

 Clustering keys in Snowflake work by sorting the designated columns over time. This process is done in the background and does not block data manipulation language (DML) operations, allowing for normal database operations to continue without interruption. The purpose of clustering keys is to organize the data within micro-partitions to optimize query performance1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Clustering1

The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory. Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.

References:

Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

QUSTION NO: 582

How do secure views compare to non-secure views in Snowflake?

A. Secure views execute slowly compared to non-secure views.

B. Non-secure views are preferred over secure views when sharing data.

C. Secure views are similar to materialized views in that they are the most performant.

D. There are no performance differences between secure and non-secure views.

Answer: D

Secure views and non-secure views in Snowflake are differentiated primarily by their handling of data access and security rather than performance characteristics. A secure view enforces row-level security and ensures that the view definition is hidden from the users. However, in terms of performance, secure views do not inherently execute slower or faster than non-secure views. The performance of both types of views depends more on other factors such as underlying table design, query complexity, and system workload rather than the security features embedded in the views themselves.

References:

Snowflake Documentation on Views: This section provides an overview of both secure and non-secure views, clarifying that the main difference lies in security features rather than performance, thus supporting the assertion that there are no inherent performance differences.

QUSTION NO: 583

When using SnowSQL, which configuration options are required when unloading data from a SQL query run on a local machine? {Select TWO).

A. echo

B. quiet

C. output_file

D. output_format

E. force_put_overwrite

Answer: C, D

When unloading data from SnowSQL (Snowflake's command-line client), to a file on a local machine, you need to specify certain configuration options to determine how and where the data should be outputted. The correct configuration options required are:

C. output_file: This configuration option specifies the file path where the output from the query should be stored. It is essential for directing the results of your SQL query into a local file, rather than just displaying it on the screen.

D. output_format: This option determines the format of the output file (e.g., CSV, JSON, etc.). It is crucial for ensuring that the data is unloaded in a structured format that meets the requirements of downstream processes or systems.

These options are specified in the SnowSQL configuration file or directly in the SnowSQL command line. The configuration file allows users to set defaults and customize their usage of SnowSQL, including output preferences for unloading data.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

Snowflake Documentation: Configuring SnowSQL at Snowflake Documentation

QUSTION NO: 584

How can a Snowflake user post-process the result of SHOW FILE FORMATS?

A. Use the RESULT_SCAN function.

B. Create a CURSOR for the command.

C. Put it in the FROM clause in brackets.

D. Assign the command to RESULTSET.

Answer: A

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes

QUSTION NO: 585

Which file function gives a user or application access to download unstructured data from a Snowflake stage?

A. BUILD_SCOPED_FILE_URL

B. BUILD_STAGE_FILE_URL

C. GET_PRESIGNED_URL

D. GET STAGE LOCATION

Answer: C

The function that provides access to download unstructured data from a Snowflake stage is:

C. GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

QUSTION NO: 586

When should a multi-cluster virtual warehouse be used in Snowflake?

A. When queuing is delaying query execution on the warehouse

B. When there is significant disk spilling shown on the Query Profile

C. When dynamic vertical scaling is being used in the warehouse

D. When there are no concurrent queries running on the warehouse

Answer: A

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

A. When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

QUSTION NO: 587

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A. select data:BIKEID

B. select data.BIKE.BIKEID

C. select data:BIKE.BIKEID

D. select data:BIKE:BIKEID

Answer: C

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

QUSTION NO: 588

Which Snowflake tool is recommended for data batch processing?

A. SnowCD

B. SnowSQL

C. Snowsight

D. The Snowflake API

Answer: B

For data batch processing in Snowflake, the recommended tool is:

B. SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

QUSTION NO: 589

How does the Snowflake search optimization service improve query performance?

A. It improves the performance of range searches.

B. It defines different clustering keys on the same source table.

C. It improves the performance of all queries running against a given table.

D. It improves the performance of equality searches.

Answer: D

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

D. It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions.

This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

References:

Snowflake Documentation: Search Optimization Service at Snowflake Documentation

QUSTION NO: 590

What compute resource is used when loading data using Snowpipe?

A. Snowpipe uses virtual warehouses provided by the user.

B. Snowpipe uses an Apache Kafka server for its compute resources.

C. Snowpipe uses compute resources provided by Snowflake.

D. Snowpipe uses cloud platform compute resources provided by the user.

Answer: C

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

Snowflake Documentation: Understanding Snowpipe

QUSTION NO: 591

What is one of the characteristics of data shares?

A. Data shares support full DML operations.

B. Data shares work by copying data to consumer accounts.

C. Data shares utilize secure views for sharing view objects.

D. Data shares are cloud agnostic and can cross regions by default.

Answer: C

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

Snowflake Documentation: Understanding Secure Views in Data Sharing

QUSTION NO: 592

Which DDL/DML operation is allowed on an inbound data share?

A. ALTER TA3LE

B. INSERT INTO

C. MERGE

D. SELECT

Answer: D

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

Snowflake Documentation: Using Data Shares

QUSTION NO: 593

In Snowflake, the use of federated authentication enables which Single Sign-On (SSO) workflow activities? (Select TWO).

A. Authorizing users

B. Initiating user sessions

C. Logging into Snowflake

D. Logging out of Snowflake

E. Performing role authentication

Answer: B C

Federated authentication in Snowflake allows users to use their organizational credentials to log in to Snowflake, leveraging Single Sign-On (SSO). The key activities enabled by this setup include:

B. Initiating user sessions: Federated authentication streamlines the process of starting a user session in Snowflake by using the existing authentication mechanisms of an organization.

C. Logging into Snowflake: It simplifies the login process, allowing users to authenticate with their organization's identity provider instead of managing separate credentials for Snowflake.

References:

Snowflake Documentation: Configuring Federated Authentication

QUSTION NO: 594

A user wants to upload a file to an internal Snowflake stage using a put command.

Which tools and or connectors could be used to execute this command? (Select TWO).

A. SnowCD

B. SnowSQL

C. SQL API

D. Python connector

E. Snowsight worksheets

Answer: B, E

To upload a file to an internal Snowflake stage using a PUT command, you can use:

B. SnowSQL: SnowSQL, the command-line client for Snowflake, supports the PUT command, allowing users to upload files directly to Snowflake stages from their local file systems.

E. Snowsight worksheets: Snowsight, the web interface for Snowflake, provides a user-friendly environment for executing SQL commands, including the PUT command, through its interactive worksheets.

References:

Snowflake Documentation: Loading Data into Snowflake using SnowSQL

Snowflake Documentation: Using Snowsight

To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

 Once an internal stage is dropped in Snowflake, it cannot be recovered or restored using Time Travel or UNDROP commands. The only option is to recreate the dropped stage

 Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, to accommodate varying workloads910. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the account level. This parameter determines the minimum number of days Snowflake retains historical data for Time Travel operations

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.

Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The purpose of the Snowflake SPLIT_TO_TABLE function is to split a string based on a specified delimiter and flatten the results into rows. This table function is useful for transforming a delimited string into a set of rows that can be further processed or queried5.

Snowflake’s Time Travel feature is used for the maintenance of historical data, allowing users to access and restore data that has been changed or deleted within a defined period4.

The GOVERNANCE_VIEWER role in the ACCOUNT_USAGE schema provides visibility into policy-related information within Snowflake. This role is specifically designed to access views that display object metadata and usage metrics related to governance12.

The DATA_SHARING_USAGE schema includes views that display information about listings published in the Snowflake Marketplace or a data exchange, which includes DATA_TRANSFER_HISTORY and LISTING_TELEMETRY_DAILY2.

 In Snowflake, if a temporary table and a permanent table have the same name within the same schema, the temporary table takes precedence over the permanent table within the session where the temporary table was created4.

 The Access History feature in Snowflake allows administrators to track data access patterns and identify unused data. This information can be used to make decisions about archiving or deleting data to optimize storage and reduce costs. 

 To view and modify resource monitors, the least privileges needed are MONITOR and MODIFY. These privileges allow a user to monitor credit usage and make changes to resource monitors3.

The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query. This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures

 A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

To determine which user has accessed a database object containing sensitive information, a Snowflake administrator can query the ACCESS_HISTORY view in the ACCOUNT_USAGE schema, which provides information about access to database objects3.

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s Data Exchange feature provides a data hub for secure data collaboration. It allows providers to publish data that can be discovered and accessed by a selected group of invited members, facilitating secure and controlled data sharing within a collaborative environment3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The OWNERSHIP privilege is unique in that it must be granted by one role to another and cannot be revoked. This ensures that the transfer of ownership is deliberate and permanent, reflecting the importance of ownership in managing access and permissions.

 When the DROP MANAGED ACCOUNT command is executed in Snowflake, it removes the managed account, including all objects created within the account, and access to the account is immediately restricted2.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

References = [COF-C02] SnowPro Core Certification Exam Study Guide

A Stream object in Snowflake is used for change data capture (CDC), which records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes3.

Snowflake stores metadata for rows in micro-partitions, including the range of values for each column and the number of distinct values1.

 A masking policy in Snowflake consists of a single data type, with one or more conditions, and one or more masking functions. These components define how the data is masked based on the specified conditions3.

The LOOP type of loop in Snowflake Scripting does not have a built-in termination condition and requires a BREAK statement to stop executing4.

The minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data is the Business Critical Edition. This edition offers enhanced security features necessary for compliance with regulations such as HIPAA and HITRUST CSF4.

The statistics that can be used to identify queries with inefficient pruning are ‘Partitions scanned’ and ‘Partitions total’. These statistics indicate how much of the data was actually needed and scanned versus the total available, which can highlight inefficiencies in data pruning34.

A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To start configuring Snowflake for Single Sign-On (SSO), the CREATE SECURITY INTEGRATION command is used. This command sets up a security integration object in Snowflake, which is necessary for enabling SSO with external identity providers using SAML 2.01.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In Snowflake, a user with the SECURITYADMIN role or higher can activate and enforce a network policy for all users in an account. Additionally, a role that has ownership of the network policy can also activate and enforce it

 For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key. This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The ORGADMIN role in Snowflake is an organizational-level role that provides administrative capabilities across the entire organization, rather than being limited to a single Snowflake account. Users with this role can:

A. Create an account for an organization: The ORGADMIN role has the privilege to create new Snowflake accounts within the organization, allowing for the expansion and management of the organization's resources.

D. View usage information for all accounts in an organization: This role also has access to comprehensive usage and activity data across all accounts within the organization. This is crucial for monitoring, cost management, and optimization at the organizational level.

References:

Snowflake Documentation: Understanding Role-Based Access Control

The LIST and PUT commands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients. LIST is used to display the contents of a stage, and PUT is used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide

When the data retention period is changed at the schema level, all child objects that do not have an explicit retention period set will inherit the new retention period from the schema4.

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

Snowflake Documentation: Querying Semi-structured Data

The worksheet and database explorer feature in Snowsight allows users to tag frequently accessed worksheets for ease of access. This functionality helps users organize and quickly navigate to the worksheets they use most often, enhancing productivity and streamlining the data exploration and analysis process within Snowsight, Snowflake's web-based query and visualization interface.

References:

Snowflake Documentation: Snowsight (UI for Snowflake)

The layer of Snowflake's architecture associated with virtual warehouses is the Query Processing layer. Virtual warehouses in Snowflake are dedicated compute clusters that execute SQL queries against the stored data. This layer is responsible for the entire query execution process, including parsing, optimization, and the actual computation. It operates independently of the storage layer, enabling Snowflake to scale compute and storage resources separately for efficiency and cost-effectiveness.

References:

Snowflake Documentation: Snowflake Architecture

For directory tables, a named external stage allows for the automatic refreshing of metadata. This capability is particularly useful when dealing with files stored on external storage services (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) and accessed through Snowflake. The external stage references these files, and the directory table's metadata can be automatically updated to reflect changes in the underlying files.

References:

Snowflake Documentation: External Stages

To unload data from a table into one or more files in a named stage, the COPY INTO <location> command should be used. This command exports the result of a query, such as selecting all rows from a table, into files stored in the specified stage. The COPY INTO command is versatile, supporting various file formats and compression options for efficient data unloading.

References:

Snowflake Documentation: COPY INTO Location

To access a file URL from an external stage in Snowflake, the minimum permission required is USAGE on the stage object. USAGE permission allows a user to reference the stage in SQL commands, necessary for actions like listing files or loading data from the stage, but does not permit the user to alter or drop the stage.

References:

Snowflake Documentation: Access Control

The TABLE_STORAGE_METRICS view can be used to determine if a table has frequent row updates or deletes. This view provides detailed metrics on the storage utilization of tables within Snowflake, including metrics that reflect the impact of DML operations such as updates and deletes on table storage. For example, metrics related to the number of active and deleted rows can help identify tables that experience high levels of row modifications, indicating frequent updates or deletions.

References:

Snowflake Documentation: TABLE_STORAGE_METRICS View

UTF-8 is the only supported character set for loading and unloading data from all supported file formats in Snowflake. UTF-8 is a widely used encoding that supports a large range of characters from various languages, making it suitable for internationalization and ensuring data compatibility across different systems and platforms.

References:

Snowflake Documentation: Data Loading and Unloading

By default, the ACCOUNTADMIN role in Snowflake has the ability to create a share from a shared database. This role has the highest level of access within a Snowflake account, including the management of all aspects of the account, such as users, roles, warehouses, and databases, as well as the creation and management of shares for secure data sharing with other Snowflake accounts.

References:

Snowflake Documentation: Roles

By default, the standard retention period for Time Travel in Snowflake is 1 day across all Snowflake accounts. Time Travel enables users to access historical data within this retention window, allowing for point-in-time data analysis and recovery. This feature is a significant aspect of Snowflake's data management capabilities, offering flexibility in handling data changes and accidental deletions.

References:

Snowflake Documentation: Using Time Travel

Scalar functions in Snowflake (and SQL in general) are designed to return a single value for each invocation. They operate on a single value and return a single result, making them suitable for a wide range of data transformations and calculations within queries.

References:

Snowflake Documentation: Functions

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

Snowflake Documentation: Data Sharing

To temporarily bypass a network policy by configuring the user object property MINS_TO_BYPASS_NETWORK_POLICY, the USERADMIN role should be used. This role has the necessary privileges to modify user properties, including setting a temporary bypass for network policies, which can be crucial for enabling access under specific circumstances without permanently altering the network security configuration.

References:

Snowflake Documentation: User Management

When initiating a session in Snowflake, the system determines the current role based on the user's connection details and role assignments. If a user specifies a role during the connection, and that role is already granted to them, Snowflake sets it as the current role for the session. Alternatively, if no role is specified during the connection, but the user has a default role assigned, Snowflake will use this default role as the current session role. These mechanisms ensure that users operate within their permissions, enhancing security and governance within Snowflake environments.

References:

Snowflake Documentation: Understanding Roles

The Fail-safe retention period for transient and temporary tables in Snowflake is 0 days. Fail-safe is a feature designed to protect data against accidental loss or deletion by retaining historical data for a period after its Time Travel retention period expires. However, transient and temporary tables, which are designed for temporary or short-term storage and operations, do not have a Fail-safe period. Once the data is deleted or the table is dropped, it cannot be recovered.

References:

Snowflake Documentation: Understanding Fail-safe

A materialized view in Snowflake is a database object derived from a query specification, stored for later use, and can significantly speed up expensive aggregations on large data sets. Materialized views store the result of their underlying query, reducing the need to recompute the result each time the view is accessed. This makes them ideal for improving the performance of read-heavy, aggregate-intensive queries.

References:

Snowflake Documentation: Using Materialized Views

In the Query Profile of Snowflake, the TableScan operator represents the access to a single table. This operator indicates that the query execution involved reading data from a table stored in Snowflake. TableScan is a fundamental operation in query execution plans, showing how the database engine retrieves data directly from tables as part of processing a query.

References:

Snowflake Documentation: Understanding the Query Profile

The Cloud Services layer in Snowflake is responsible for a wide range of services that facilitate the management and use of Snowflake, including:

D. User authentication: This service handles identity and access management, ensuring that only authorized users can access Snowflake resources.

E. Infrastructure management: This service manages the allocation and scaling of resources to meet user demands, including the management of virtual warehouses, storage, and the orchestration of query execution.

These services are part of Snowflake's fully managed, cloud-based architecture, which abstracts and automates many of the complexities associated with data warehousing.

References:

Snowflake Documentation: Overview of Snowflake Cloud Services

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

Snowflake Documentation: Query Performance Optimization

A Pre-signed URL provides access to files stored in Snowflake without requiring authorization at the time of access. This feature allows users to generate a URL with a limited validity period that grants temporary access to a file in a secure manner. It's particularly useful for sharing data with external parties or applications without the need for them to authenticate directly with Snowflake.

References:

Snowflake Documentation: Using Pre-signed URLs

In the Snowflake Web Interface (UI), the Query History page displays the history of queries executed in Snowflake for up to 14 days. This allows users to review and analyze their query performance, troubleshoot issues, and understand their query patterns over a two-week period. The Query History page is a critical tool for monitoring and optimizing the use of Snowflake.

References:

Snowflake Documentation: Using the Web Interface

Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:

Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.

Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.

Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.

The Query Profile in Snowflake provides a graphical representation and statistics for each component of the query's execution plan. This includes details such as the execution time, the number of rows processed, and the amount of data scanned for each operation within the query. The Query Profile is a crucial tool for understanding and optimizing the performance of queries, as it helps identify potential bottlenecks and inefficiencies.

References:

Snowflake Documentation: Understanding the Query Profile

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

References:

Snowflake Documentation: Virtual Warehouses Billing

A cached MFA token is valid for up to four hours. https://docs.snowflake.com/en/user-guide/security-mfa#using-mfa-token-caching-to-minimize-the-number-of-prompts-during-authentication-optional

When creating a CSV file format in Snowflake and the requirement is to wrap columns by single quotes or double quotes, the FIELD_OPTIONALLY_ENCLOSED_BY parameter should be used in the file format specification. This parameter allows you to define a character (either a single quote or a double quote) that can optionally enclose each field in the CSV file, providing flexibility in handling fields that contain special characters or delimiters as part of their data.

References:

Snowflake Documentation: CSV File Format

Network policies in Snowflake are defined as a set of rules that manage the network-level access to Snowflake accounts. These rules specify which IP addresses or IP ranges are permitted to connect to Snowflake, enhancing the security of Snowflake accounts by preventing unauthorized access. Network policies are an essential aspect of Snowflake's security model, allowing administrators to enforce access controls based on network locations.

References:

Snowflake Documentation: Network Policies

In Snowflake, when setting up a network policy that specifies both allowed and blocked IP address lists, if an IP address appears in both lists, access from that IP address will be denied. The reason is that Snowflake prioritizes security, and the presence of an IP address in the blocked list indicates it should not be allowed regardless of its presence in the allowed list. This ensures that access controls remain stringent and that any potentially unsafe IP addresses are not inadvertently permitted access.

References:

Snowflake Documentation: Network Policies

The command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

Snowflake Documentation: SHOW FILE FORMATS

Temporary tables in Snowflake do not consume storage costs. They are designed for transient data that is needed only for the duration of a session. Data stored in temporary tables is held in the virtual warehouse's cache and does not persist beyond the session's lifetime, thereby not incurring any storage charges.

References:

Snowflake Documentation: Temporary Tables

To extract the URL of a directory table on an external stage for further transformation in Snowflake, the GET_ABSOLUTE_PATH function can be used. This function returns the full path of a file or directory within a specified stage, enabling users to dynamically construct URLs for accessing or processing data stored in external stages.

References:

Snowflake Documentation: Working with Stages

The replication feature in Snowflake provides several benefits, with disaster recovery and database failover and fallback being two of the primary advantages. Replication allows for the continuous copying of data from one Snowflake account to another, ensuring that a secondary copy of the data is available in case of outages or disasters. This capability supports disaster recovery strategies by allowing operations to quickly switch to the replicated data in a different account or region. Additionally, it facilitates database failover and fallback procedures, ensuring business continuity and minimizing downtime.

References:

Snowflake Documentation: Data Replication

Purpose: The BUILD_STAGE_FILE_URL function generates a temporary, pre-signed URL that allows you to access a file within a Snowflake stage (internal or external). This URL requires authentication to use.

Key Points:

Security: The URL has a limited lifespan, enhancing security.

Use Cases: Sharing staged data with external tools or applications, or downloading it directly.

Snowflake Documentation (BUILD_STAGE_FILE_URL): https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys

In Snowflake, the maximum number of clusters that can be provisioned within a multi-cluster virtual warehouse is 10. This allows for significant scalability and performance management by enabling Snowflake to handle varying levels of query load by adjusting the number of active clusters within the warehouse.References: Snowflake documentation on virtual warehouses, particularly the scalability options available in multi-cluster configurations.

Valid sub-clauses to the OVER clause for a window function in SQL are:

C. ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.

D. PARTITION BY: This clause divides the result set into partitions to which the window function is applied. Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.

These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.

References:

Snowflake Documentation: Window Functions

A materialized view in Snowflake denotes a pre-computed data set derived from a SELECT query specification and stored for later use. Unlike standard views, which dynamically compute the data each time the view is accessed, materialized views store the result of the query at the time it is executed, thereby speeding up access to the data, especially for expensive aggregations on large datasets.

References:

Snowflake Documentation: Materialized Views

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

B. Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.

C. Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

References:

Snowflake Documentation: Transient Tables

In Snowflake, stored procedures and User-Defined Functions (UDFs) have different invocation patterns within SQL:

Option B is correct: A single executable statement can call only one stored procedure due to the procedural and potentially transactional nature of stored procedures. In contrast, a single SQL statement can call multiple UDFs because UDFs are designed to operate more like functions in traditional programming, where they return a value and can be embedded within SQL queries.References: Snowflake documentation comparing the operational differences between stored procedures and UDFs.

The SYSTEM$GET_PRIVATELINK function in Snowflake provides proxy information necessary for configuring PrivateLink connections, which can protect Snowsight as well as other Snowflake services. PrivateLink enhances security by allowing Snowflake to be accessed via a private connection within a cloud provider’s network, reducing exposure to the public internet.

References:

Snowflake Documentation: PrivateLink Setup

 Transient tables in Snowflake have a minimum Fail-safe retention time period of 0 days. This means that once the Time Travel retention period ends, there is no additional Fail-safe period for transient tables

 The Snowflake Cloud Services layer is a collection of services that coordinate activities across Snowflake, tying together all the different components to process user requests, from login to query dispatch1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

Operations that do not require compute resources are typically those that can leverage previously cached results. However, if no queries have been executed previously, all the given operations would require compute to execute. It’s important to note that certain operations like DDL statements and queries that hit the result cache do not consume compute credits2.

The minimum Snowflake edition required to use Dynamic Data Masking is the Enterprise edition. This feature is not available in the Standard edition2.

 Snowflake stored procedures can be created to execute with the privileges of the role that owns the procedure (owner’s rights) or with the privileges of the role that calls the procedure (caller’s rights). This allows for flexibility in managing security and access control within Snowflake1.

Snowflake virtual warehouses support a configuration file and command line options in SnowSQL to specify a default warehouse, which is characteristic C. Additionally, the size of a virtual warehouse can be changed at any time, which is characteristic E. These features provide flexibility and ease of use in managing compute resources2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

Column-level security, which allows for the application of masking policies to columns in tables or views, is available starting from the Enterprise edition of Snowflake1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

The ACCESS_HISTORY view in the SNOWFLAKE.ACCOUNT_USAGE schema contains information about the access history of Snowflake objects, such as tables and views, within the last 365 days1.

 The credit consumption for maintaining a materialized view is impacted by how often the base table changes © and whether the materialized view has a cluster key defined (D). Changes to the base table can trigger a refresh of the materialized view, consuming credits. Additionally, having a cluster key defined can optimize the performance and credit usage during the materialized view’s maintenance. References: SnowPro Core Certification materialized view credit consumption