You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

• A. Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
• B. Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.
• C. Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.
• D. Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.
• E. Manually partitioning the DataFrame into smaller chunks before applying transformations.

You have a Snowpark Python application that reads data from multiple Snowflake tables, performs complex transformations using UDFs, and writes the results to a new table. During peak hours, the application experiences performance bottlenecks. The Snowflake warehouse associated with the Snowpark session is already configured with the 'SNOWPARK OPTIMIZED warehouse type. Which of the following strategies, when implemented together, would BEST improve the application's performance?

• A. Enable query acceleration and increase the 'MAX CONCURRENCY LEVEL' session parameter.
• B. Increase the size of the Snowpark-optimized warehouse and enable auto-scaling with a minimum of 1 node and a maximum of 2 nodes.
• C. Increase the size of the Snowpark-optimized warehouse, utilize vectorized UDFs where applicable, and consider using Snowpark's optimized join operations (if available).
• D. Use the 'CACHE RESULT clause for frequently accessed data and rewrite UDFs in SQL instead of Python.
• E. Increase the size of the Snowpark-optimized warehouse, partition the input tables based on relevant join keys, and optimize UDFs for CPU efficiency.

Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

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You are working with image files stored in a Snowflake internal stage named 'image_stage'. You need to write a Snowpark Python application to resize these images using a Python library called 'PIG. The resizing logic is encapsulated in a function called resize_image(snowflake_file: SnowflakeFile, width: int, height: int) -> bytes. Which of the following code snippets correctly registers he 'resize image' function as a UDF and applies it to the image files?

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You have created a Snowpark stored procedure in Python that accesses a Snowflake stage to read configuration files. To enhance security, you want to grant the stored procedure specific permissions to only read files from that stage, without granting broader account- level access. Which of the following approaches is the MOST secure and granular way to achieve this?

• A. Grant the 'READ privilege directly to the stored procedure using the 'ALTER PROCEDURE command.
• B. Grant the 'USAGE privilege on the stage to the role that owns the stored procedure.
• C. Create a stored procedure with 'EXECUTE AS CALLER and ensure the caller (user executing the procedure) has the necessary privileges.
• D. Create a Snowflake custom role and grant the 'READ' privilege on the stage to this role, then grant this role to the stored procedure's owner.
• E. Grant the 'SELECT privilege on the stage to the role that owns the stored procedure.