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Snowflake SnowPro Specialty - Native Apps Sample Questions (Q346-Q351):

NEW QUESTION # 346
You are designing a Snowflake Native Application using Snowpark Container Services to perform real-time sentiment analysis on streaming dat a. The application needs to subscribe to a Snowflake event table to receive incoming data, perform the analysis in the container, and store the results in another table in the consumer's account. Which of the following approaches represents the most efficient and scalable way to implement this data ingestion and processing pipeline?

A. Implement a Snowpipe pipeline that continuously loads data from an external stage into the event table. Then use a database trigger on the event table to invoke a stored procedure that calls the Snowpark Container Service.
B. Create a Snowflake external table that directly reads data from the external source. Mount this external table within the container service and perform sentiment analysis directly on the external data.
C. Utilize Snowflake Event Tables and subscribe the Snowpark Container Service to the event table. Process incoming events using a Snowpark Python function within the container, leveraging its ability to handle concurrent requests.
D. Use a Snowflake Stream on the event table and continuously poll the Stream from within the Snowpark Container Service using the Snowpark API.
E. Configure a Snowflake Task to periodically load data from the event table into an internal stage and then trigger the Snowpark Container Service to process the data from the stage.

Answer: C

Explanation:
The most efficient and scalable approach for real-time data ingestion and processing in this scenario is to use Snowflake Event Tables and subscribe the Snowpark Container Service to the event table. Here's why: D: Correct. Snowflake Event Tables provide a native mechanism for capturing data changes, which is ideal for streaming data. Subscribing the Snowpark Container Service to the event table allows it to react in real time to new data. Leveraging the container's ability to handle concurrent requests ensures scalability. A: Incorrect. Continuously polling a Snowflake Stream is inefficient and can lead to unnecessary resource consumption. It doesn't provide a real-time processing capability. B: Incorrect. Using a Snowflake Task to periodically load data introduces latency and is not suitable for real-time analysis. C: Incorrect. While Snowpipe is designed for continuous data loading, using a database trigger to invoke a stored procedure that calls the Snowpark Container Service can introduce performance bottlenecks and complexity. E: Incorrect. External tables are typically used for querying data stored outside of Snowflake, not for real-time data ingestion and processing.

NEW QUESTION # 347
You are developing a Snowflake Native Application that needs to access data residing in the consumer's account. The application requires specific permissions to read certain tables and execute specific functions. How should you define and manage these access privileges to ensure both security and functionality?

A. Grant the 'ACCOUNTADMIN' role to the application. This provides unrestricted access to all resources in the consumer's account, simplifying development and deployment.
B. Define a custom role within the application package with the minimum required privileges (e.g., 'SELECT on specific tables, 'USAGE on specific functions). The consumer then grants this role to the application during installation. Use secure views if needed.
C. Use the 'GRANT OWNERSHIP' command to transfer ownership of the required tables and functions to the application's service account. This gives the application full control over these resources.
D. Create a new database role and grant the required permissions to the database role, later grant the database role to the application role. Use secure views for access to consumer data.
E. Rely on the consumer to manually grant the necessary privileges to the application's service account after installation. Provide clear instructions in the application's documentation.

Answer: B,D

Explanation:
Options B and E are the most secure and maintainable approaches. Option B follows the principle of least privilege. Option E will add another layer of security. Option A is a very bad practice. Option C is also incorrect, transferring ownership is not a good idea. Option D makes the configuration prone to manual errors.

NEW QUESTION # 348
A consumer installs a Snowflake Native Application from the Marketplace. The application requires access to a specific table, 'CUSTOMER DATA' , in the consumer's account. Which of the following steps are essential for the consumer to properly grant the application the necessary permissions, following least privilege principles, and ensuring the application functions as intended? Assume the application developer has properly requested the table grant in the setup script.

A. Grant the 'USAGE privilege on the database and schema containing 'CUSTOMER_DATA' to the application role and then grant 'SELECT on the 'CUSTOMER DATA table to the application role.
B. Grant the 'IMPORTED PRIVILEGES privilege on the database containing 'CUSTOMER_DATX to the application role created during installation.
C. Grant the 'SELECT privilege directly on the 'CUSTOMER_DATA' table to the 'SNOWFLAKE.APP_ROLE' role associated with the application instance.
D. Grant 'OWNERSHIP' on the "CUSTOMER_DATR table to the application role. This provides the application with complete control over the table.
E. Create a share from the provider account granting access to the 'CUSTOMER DATA' table and import it into the consumer account.

Answer: A

Explanation:
Option D is correct because it grants the minimum required privileges for the application to access the 'CUSTOMER_DATX table. Granting USAGE' on the database and schema allows the application to navigate to the table. Granting 'SELECT' on the table itself allows the application to read the data. Options A, B, C and E are incorrect because they either grant excessive privileges, are not the correct approach for Native Apps (Shares), or grant permissions to the wrong role.

NEW QUESTION # 349
You are developing a Snowflake Native Application and need to track changes made to the application code across different versions. You want to leverage Snowflake's built-in capabilities for version control and auditing. Which of the following approaches is the MOST suitable and efficient for achieving this?

A. Use Snowflake's Time Travel feature to query historical versions of application code objects (e.g., stored procedures, UDFs) and compare differences between versions.
B. Use Snowflake's Streams feature to capture changes made to application code objects and store them in a separate change table for auditing.
C. Integrate with an external version control system (e.g., Git) and store application code and deployment scripts in the repository. Use CI/CD pipelines to manage deployments to Snowflake.
D. Manually maintain a change log in a separate Snowflake table, documenting each code modification with associated version numbers and timestamps.
E. Utilize Snowflake's Information Schema views (e.g., 'INFORMATION_SCHEMA.PROCEDURES, 'INFORMATION SCHEMA.FUNCTIONS) in conjunction with Time Travel to query historical metadata and code definitions.

Answer: E

Explanation:
Option E is the most suitable and efficient solution. Combining Snowflake's Information Schema views with Time Travel provides a comprehensive and native way to track changes to application code objects. The Information Schema provides metadata about database objects, and Time Travel allows you to query historical versions of those objects. This approach leverages Snowflake's built-in capabilities without requiring external tools or manual change logging. Options A and B requires extra configuration of the whole process. Option C while feasible, Time Travel has to be enabled on each component, which makes option E superior. Option D is designed for data and not code auditing.

NEW QUESTION # 350
Your Snowflake Native Application provides a data transformation service. A consumer reports slow performance when processing large datasets. Profiling indicates that a specific SQL query within a UDF is the bottleneck. The query aggregates data from a table and uses a custom function to calculate a derived value. Assume the UDF runs with invoker rights. Which of the following strategies, used in combination, would MOST effectively improve the query's performance while adhering to security best practices for native applications and least privileges?

A. Rewrite the UDF to use a SQL UDF instead of a Python UDF, grant the necessary privileges to the application's invoker role, and create appropriate indexes on the underlying table in consumer account.
B. Replace the UDF with a materialized view in the consumer's account that pre-computes the derived value, and grant SELECT privilege on the materialized view to the consumer role.
C. Rewrite the UDF to use a SQL UDF instead of a Python UDF, create a secure view exposing only the necessary data, and use clustering on the underlying consumer table.
D. Implement caching for the custom function's results using Snowflake's result caching mechanism, rewrite the UDF to use a SQL UDF instead ofa Python UDF, and utilize a data access framework using secure view, which enables the invoker right to be succesfull.
E. Distribute the processing workload by using Snowflake's Snowpark to create a DataFrame, distribute using custom python code and scale out.

Answer: C

Explanation:
Option E is the most effective and secure. Converting the UDF to a SQL UDF improves performance, since it runs within Snowflake's engine, reducing overhead. A secure view limits access to the required data, adhering to least privilege. Clustering optimizes query performance on the consumer's data. Indexes(option A) might help, but clustering is better for large datasets. Result caching (Option B) is good, but not a substitute for efficient query design and sql udf. Materialized views (Option C) are controlled by the consumer and outside the control of the provider. Snowpark(Option D) can work but it would require a complex redesign.

NEW QUESTION # 351
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