You are developing a Native App on Snowflake that collects usage data from consumer accounts. You need to store this data securely and efficiently, and you anticipate that the volume of data will grow rapidly. Which of the following strategies would you consider to OPTIMIZE storage and query performance for this usage data within the application?

• A. Store all usage data in a single table and rely on Snowflake's automatic clustering to handle performance.
• B. Implement a data retention policy to automatically purge older usage data that is no longer needed.
• C. Use clustering keys on columns commonly used in queries, such as timestamp and consumer account ID. Consider data compression options if available.
• D. Create separate tables for each consumer account to store their usage data.
• E. Partition the usage data table by consumer account ID to isolate data from different consumers.

You are packaging a Snowflake Native Application for distribution. You need to include a Python User-Defined Function (UDF) that reads data from a stage. Which of the following steps are REQUIRED to ensure the UDF functions correctly after installation in a consumer's account? (Choose two)

• A. Ensure the stage containing the Python file is explicitly referenced with 'OWNER' rights in the application manifest file.
• B. Explicitly grant 'USAGE privilege on the stage to each role within the application that will execute the UDF in the 'setup.sqr script.
• C. Include the Python code for the UDF inline within the 'CREATE FUNCTION' statement, instead of referencing an external stage.
• D. Grant the 'READ privilege on the stage to the schema in the provider account before building the application package.
• E. Grant on the stage and 'USAGE' on the database and schema containing the stage to the 'SNOWFLAKCORE.BUILTIN APP API' schema in the provider account before building the application package.

You are designing a Streamlit application within a Snowflake Native App package. This application displays data from a customer's Snowflake account. The application needs to enforce strict data governance policies, ensuring that users only see the data they are authorized to view based on their roles and permissions within the customer's account. Which strategies and Snowflake features should be employed to achieve this?

• A. Use external tables to fetch the data.
• B. Use Snowflake's masking policies to redact sensitive data based on the user's role and context at the database level.
• C. Create separate Streamlit applications for each user role, each with its own data access configuration.
• D. Implement row-level security policies directly within the Streamlit application using Python code to filter the data.
• E. Leverage Snowflake's row access policies to automatically filter data based on the user's role and context at the database level.

You are packaging a Snowflake Native App that includes a Streamlit application for data visualization. The Streamlit application needs to access a table named 'SALES DATA within the app's data container. Which of the following steps are REQUIRED to correctly grant the Streamlit application the necessary permissions to read the data? (Select all that apply)

• A. Grant the 'APPLOG' privilege to the application role defined in the setup script on the internal stage.
• B. Create a secure view on the ' SALES_DATX table, using the function in the view definition, and grant the 'SELECT privilege on the secure view to the application role.
• C. Grant the 'SELECT privilege on the 'SALES_DATX table directly to the application role defined in the setup script.
• D. Create a service account within the Snowflake Native App and grant the 'SELECT' privilege on the 'SALES_DATR table to the service account.
• E. Grant the 'IMPORTED PRIVILEGES' on the database containing the 'SALES DATA' table to the application role.

You are developing a Snowflake Native App that uses JavaScript stored procedures for complex data transformations. You want to implement robust error handling and logging to ensure that you can effectively troubleshoot issues in both provider and consumer environments. Which of the following strategies provide the MOST comprehensive approach to error handling and logging in this scenario? (Select TWO)

• A. Rely solely on the error messages displayed in the Snowflake web UI when a stored procedure fails. These messages are sufficient for most debugging scenarios.
• B. Implement a custom error tracking system using external functions to send error information to a third-party logging service.
• C. Use 'CALL SYSTEM$LOG(...)'})$ within your JavaScript stored procedures to log messages at different levels (e.g., INFO, WARN, ERROR) directly to the Snowflake event table. Implement structured logging for easier analysis.
• D. Configure Snowflake's built-in query history to capture all query executions and analyze the query text for errors. No explicit logging within the stored procedures is necessary.
• E. Use 'TRY...CATCH' blocks within your JavaScript stored procedures to catch exceptions. Log the exception message and stack trace to a dedicated logging table in the provider's account.