You are developing a Snowflake Native App that needs to access a secured external API to enrich customer dat a. The API requires an OAuth 2.0 token stored securely. Which of the following approaches would you use within the application manifest and Snowflake objects to securely store and access this token, minimizing exposure and adhering to best practices?

• A. Store the OAuth 2.0 token as a masked comment within the application's SQL code. This will provide sufficient obfuscation.
• B. Store the OAuth 2.0 token directly in the application's manifest file as a PARAMETER. This simplifies access within the application code.
• C. Hardcode the OAuth 2.0 token within the application's stored procedures. This ensures the token is readily available.
• D. Store the OAuth 2.0 token as a cleartext secret in a Snowflake stage and access it using external functions.
  
• E. Use Snowflake's SECRET object type to securely store the OAuth 2.0 token. Reference the SECRET object within the application using PARAMETERS in the manifest and utilize function in application code to retrieve it at runtime.

A customer has installed your Snowflake Native Application, which utilizes a IJDF that performs complex calculations. The customer reports performance issues and sporadic errors. As the application provider, how can you BEST establish telemetry and observability to diagnose the problem, without compromising the customer's data security or privacy?

• A. Instruct the customer to enable verbose logging in their Snowflake account and provide you with the log files for analysis.
• B. Create a shared view on the UDF execution logs in the customer's account, granting you read-only access to the relevant information.
• C. Request direct access to the customer's Snowflake account to inspect the IJDF execution logs and query performance metrics.
• D. Utilize Snowflake's Query History to track the IJDF's performance in the customer's account and provide recommendations based on the observed query patterns.
• E. Implement logging within the UDF to capture execution time, input parameters (anonymized), and error messages. Store this data in an Event Table within the application provider's account and expose aggregate statistics through a secure API endpoint for the consumer to monitor.

A Snowflake Native App provider has implemented an Event Table to capture application logs. They observe a significant performance degradation in their application queries when the Event Table grows beyond a certain size. Which of the following strategies can be implemented to mitigate the performance issue, assuming that frequent querying of the last 7 days of data is common? Select ONLY ONE answer.

• A. Implement a stored procedure that manually copies the last 7 days of data to a separate, smaller table.
• B. Create a materialized view on top of the Event Table that filters for events within the last 7 days.
• C. Disable automatic clustering on the Event Table.
• D. Partition the Event Table by date, using the ' INSTALLATION_TIMESTAMP' column.
• E. Increase the warehouse size used for querying the Event Table.

You are developing a Snowflake Native Application that processes customer orders. The application needs to securely store sensitive customer data (e.g., credit card numbers) within the provider's account but must grant the consumer's account access to analyze aggregated, de-identified order statistics. Which combination of features is BEST suited to achieve this while adhering to security best practices and Snowflake Native App constraints?

• A. Store sensitive data in a table within the provider account. Create a stored procedure that aggregates and de-identifies the data, writing the results to a new table. Share this new, aggregated table with the consumer account via a data share.
• B. Store sensitive data in a table within the provider account. Use dynamic data masking policies to de-identify the data when accessed by the consumer account. Share the underlying table directly with the consumer account through a share.
• C. Store sensitive data in a secured view within the provider account. Grant the consumer account usage privileges on this secured view. Create a setup script using 'CREATE APPLICATION ROLE and 'GRANT to give the consumer's application role access to the secured view.
• D. Store sensitive data in a table within the provider account. Create a user-defined function (UDF) that aggregates and de-identifies the data. Share the table and the UDF with the consumer account through a share. The consumer application can then use the UDF to query the data.
• E. Store sensitive data in an internal stage within the provider account. Create a stored procedure that reads data from the stage, aggregates and de-identifies it, and writes the results to a shareable table. The consumer application can query the shareable table.

You are developing a Snowflake Native App that requires uploading multiple large data files (each > 50MB) to a stage for processing. Due to network limitations, you need to optimize the upload process. You have already created the stage and have necessary privileges. Which of the following approaches would be most effective for uploading the files efficiently?

• A. Write a Python script leveraging the Snowflake Connector for Python to upload the files using multi-threading or asynchronous operations.
• B. Use the 'PUT ' command with the 'PARALLEL' option enabled in SnowSQL.
  
• C. Use the Snowflake Visual Studio Code (VS Code) extension's built-in file upload feature, which automatically handles parallel uploads.
• D. Utilize Snowflake's internal staging using 'PUT command on Internal stage for smaller file and external stage for larger file and then copy the larger file from external to internal by referencing file URL.
• E. Compress the files into a single ZIP archive before uploading using the 'PUT' command.