When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

• A. Grant broad access to the environment variable containing the private key to all developers.
• B. Regularly rotate the key pair.
• C. Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
• D. Encrypt the private key at rest.
• E. Store the private key directly in the Snowpark code repository.

You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

• A. Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
• B. Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
• C. Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
• D. Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
• E. Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.

You are working with a Snowpark application designed to process data from an event table. While testing a complex transformation involving several joins and window functions, you encounter the following error: 'java.lang.OutOfMemoryError: Java heap space'. The application uses Snowpark DataFrames and is running on a reasonably sized virtual warehouse. What is the MOST likely cause of this error in the context of Snowpark and Snowflake?

• A. Snowflake's internal query optimizer has generated a suboptimal execution plan, leading to excessive intermediate data materialization.
• B. An inefficient UDF (User-Defined Function) is consuming excessive memory within the Java runtime.
• C. There's a circular dependency in the DataFrame transformations, causing an infinite loop and memory leak.
• D. The virtual warehouse is undersized for the volume of data being processed, leading to excessive spilling to disk and eventual memory exhaustion on the driver node.
• E. The Snowpark driver process is attempting to load the entire result set into memory, exceeding the available heap space.

You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

• A. Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
• B. Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.
• C. Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.
• D. Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.
• E. Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.