You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)

• A. Ensure that the stored procedure is created with the 'EXECUTE AS CALLER clause.
• B. Grant the USAGE privilege on the secret to the role that will execute the stored procedure.
• C. Use the method within the stored procedure to retrieve the secret value.
• D. Create a UDF that exposes the secret and call that UDF in the stored procedure.
• E. Store the secret value directly in the stored procedure's code as a global variable.

You are tasked with building a Snowpark Python application to process JSON files stored in a Snowflake stage. The JSON files contain customer feedback data, including sentiment scores. You need to create a stored procedure that reads the JSON files, calculates the average sentiment score, and stores the result in a Snowflake table. You also need to handle potential errors, such as invalid JSON format in some files, and continue processing other files. Which of the following approaches is MOST efficient and robust to handle this scenario?

• A. Use and return a DataFrame containing the average sentiment score and file name for each processed file. Handle JSON parsing errors by skipping the file and logging the error. Use to write the DataFrame to the target table.
• B. Implement an external function using AWS Lambda or Azure Functions to parse the JSON files and calculate the average sentiment score. Call this external function from the stored procedure. This provides better error handling and scalability.
• C. Download the JSON files to the Snowpark client, process them using standard Python libraries (like 'json'), and then upload the aggregated data back to Snowflake using 'session.write_pandas()'. Handle errors locally and log them.
• D. Load the JSON data into a VARIANT column in a temporary table. Use a Snowpark DataFrame transformation to parse the JSON data from the VARIANT column. Catch errors during the DataFrame transformation process and log them to a separate table. Finally, calculate the average sentiment score using Snowpark functions.
• E. Use the function directly within the stored procedure to parse each JSON file. Catch exceptions within the loop and log errors to a separate table. Use 'DataFrame.write.mode('append')' to write the average sentiment score to the target table.

You are setting up a VS Code development environment for Snowpark with the Snowflake extension. You want to ensure that you can securely authenticate to Snowflake and execute Snowpark code. Which of the following steps are essential to configure secure authentication within VS Code for Snowpark?

• A. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Snowflake Native Authentication. Ensure that the user has the required permissions to authenticate using this method.
• B. Install the Snowflake VS Code extension and configure the Snowflake connection settings in the extension's configuration file using username and password.
• C. Install the Snowflake VS Code extension and configure the connection settings to use OAuth. Ensure the OAuth client and secret are properly configured in Snowflake and referenced in the connection settings.
• D. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use MFA. Ensure the username and password is provided with a valid MFA token.
• E. Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Key Pair authentication. Ensure the private key is securely stored and referenced in the connection settings.

You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.

• A.
  
• B.
  
• C.
  
• D.
  
• E.