You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

• A. Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
• B. Create a UDF and grant USAGE on the stage to the role that owns the UDF.
• C. Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
• D. Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
• E. Create an external function and grant access to the API integration that provides the security context.

You are building a Snowpark Python application to perform complex data transformations and want to leverage external packages not pre-installed in the Snowflake environment. You need to ensure these packages are available within your Snowpark session. Which of the following methods are valid for deploying and using these third-party packages within your Snowpark Python environment? (Select TWO)

• A. Manually install the packages on the Snowflake compute pool nodes before starting the Snowpark session.
• B. Utilize Snowflake's Anaconda channel integration and specify the package names as strings in the method. Snowflake will automatically resolve and install the packages from the Anaconda channel.
• C. Deploy the required packages using the SnowCLl package management commands, and then the Snowpark session will be able to automatically use the deployed packages.
• D. Use the 'session.addDependency()' method to upload individual '.py' files containing the package code directly to the Snowflake internal stage.
• E. Create a 'conda' environment file ('environment.yml') specifying the required packages and use the method to upload the environment definition. Snowflake will automatically install the packages within the session's environment.

A data scientist has developed a complex machine learning model in Python that needs to be operationalized within a Snowpark pipeline. This model depends on several custom Python packages not available in Snowflake's default environment. The data scientist wants to define a UDTF to apply this model to incoming data'. Which of the following steps are NECESSARY to successfully deploy and execute this UDTF in Snowflake? (Select three)

• A. Upload the ZIP file to a Snowflake stage.
• B. Specify the stage location in the 'imports' clause of the 'CREATE FUNCTION' statement when defining the UDTF.
• C. Create a virtual environment and install all the required Python packages.
• D. Include only custom packages into the ZIP file and exclude common python library packages, as snowpark is pre-installed.
• E. Package the virtual environment as a ZIP file.

You are developing a Snowpark application that utilizes a DataFrame named 'transactions df containing transactional data. You need to apply a series of complex transformations, including window functions and joins with other DataFrames. To optimize performance and manage resources effectively, you want to control how Snowpark executes these operations within Snowflake. Which of the following actions or configurations would have the MOST significant impact on controlling the execution plan and resource utilization of your Snowpark application?

• A. Use the 'DataFrame.explain()' method to analyze the generated SQL query plan before executing the transformations. Then, manually optimize the code based on the query plan output.
• B. Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.
• C. Implement iterative algorithms within your Snowpark application using imperative Python loops instead of declarative DataFrame operations. This provides finer-grained control over the execution flow.
• D. Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.
• E. Configure the 'net.snowflake.snowpark.use_native_execution' parameter to 'true' at the session level. This forces Snowpark to translate DataFrame operations into native Snowflake SQL queries.