A Snowflake Native Application provider has encountered performance issues with a Scala-based UDF, 'aggregate data' , used within their application. Consumers report slow execution times when processing large datasets. The provider has identified that the UDF involves complex calculations and data transformations. Which optimization techniques can be applied to improve the performance of the 'aggregate_data' UDF within the constraints of the Snowflake Native Application environment?

• A. Utilize Snowflake's vectorized UDF feature (if supported for Scala UDFs) to process data in batches, reducing the overhead of calling the UDF for each individual row.
• B. Optimize the UDF's data structures and algorithms to reduce memory usage and improve processing efficiency. Consider using techniques such as caching frequently accessed data within the UDF.
• C. Leverage Scala's parallel collections (e.g., 'par') to distribute the workload across multiple cores during data processing. Ensure that the UDF is designed to be thread-safe.
• D. Replace the Scala UDF with a Snowflake stored procedure written in SQL. Stored procedures generally offer better performance for data-intensive operations.
• E. Move the entire UDF logic to an external cloud function (e.g., AWS Lambda, Azure Function) and invoke it from Snowflake using a remote service. This offloads the computation to a more powerful environment.

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

You are developing a Snowflake Native Application that includes a Python module for data transformation. This module relies on the pandas' and 'requests' libraries. You are using an 'environment.yml' file to manage dependencies. Which of the following 'environment.yml' configurations is the MOST secure and correct way to define these dependencies for your application? Assume you want to install these packages directly from conda-forge channel.

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You are developing a Snowflake Native Application that processes sensitive customer dat a. You need to ensure that the application logs all relevant events for auditing and debugging purposes, while adhering to data privacy regulations. Which of the following strategies would BEST facilitate secure and compliant logging within the application provider's environment?

• A. Implement logging using standard Python logging libraries within the application code and direct the logs to stdout/stderr. Rely on the consumer to configure logging infrastructure within their account to capture the output.
• B. Utilize Snowflake's Event Tables with appropriate masking policies to redact sensitive information before logging. Configure a data retention policy that complies with regulatory requirements. Define alerts based on specific event types to proactively address potential issues within the application.
• C. Send logs to an external logging service via API calls, ensuring the service is SOC 2 compliant and offers end-to-end encryption. Consumers configure the service to receive logs and set up audit trails.
• D. Encrypt all sensitive data within the logs using a key managed by the consumer before writing to a standard Snowflake table within the application container. Provide the consumer with the decryption key upon request.
• E. Log all events directly to a Snowflake table within the application's container, granting direct access to the consumer account for auditing.

You are designing a Snowflake Native App that includes a Streamlit I-Jl. The Streamlit app needs to access data stored in a secure table within the consumer's account. What are the recommended and MOST secure methods for granting the Streamlit app access to this data, considering the principle of least privilege? Select all that apply.

• A. Grant *SELECT privilege directly on the table to the application role.
• B. Grant the ' IMPORTED PRIVILEGES' privilege on the database containing the table to the application role.
• C. Create an external function that retrieves the data and grant 'USAGE on the external function to the application role.
• D. Create a secure view on top of the table and grant 'SELECT privilege on the view to the application role. Publish the View via 'show published objects in application package'.