You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

• A. The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
• B. The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
• C. When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
• D. The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
• E. The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.

A data engineering team wants to deploy a Snowpark Python stored procedure that aggregates sales data from a table 'SALES DATA and writes the results to a table 'AGGREGATED SALES. The stored procedure needs to be executed by various users with different roles. The team wants to ensure that users can only execute the stored procedure and cannot directly access the underlying 'SALES DATA' table. Which approach is most suitable for managing data access and security in this scenario, and what are the implications of using 'EXECUTE AS OWNER vs 'EXECUTE AS CALLER?

• A. Create the stored procedure with 'EXECUTE AS CALLER and grant 'SELECT privilege on the 'SALES DATA' table to all roles that need to execute the stored procedure. This allows the stored procedure to execute with the caller's privileges, and the caller must have the necessary privileges to access the underlying tables.
• B. Create the stored procedure with 'EXECUTE AS OWNER and grant 'USAGE privilege on the database and schema containing the stored procedure to the roles that need to execute it. This hides the underlying table from the caller, and the stored procedure executes with the owner's privileges.
• C. Create the stored procedure with 'EXECUTE AS OWNER , grant 'USAGE on the database and schema. Grant 'EXECUTE TASK' on the stored procedure to the specific roles, while the Owner(Role with Execute Task permission) should have access to SALES DATA table.
• D. Create a view on the 'SALES_DATX table that only exposes the necessary columns and grant 'SELECT privilege on the view to the roles that need to execute the stored procedure. Create the stored procedure with EXECUTE AS CALLER to leverage the view's column restrictions.
• E. Create the stored procedure with 'EXECUTE AS CALLER and grant 'USAGE on the database and schema. The callers must have access to both the AGGREGATED SALES and SALES DATA tables. The stored procedure will use the caller's privileges for all operations.

You have developed a Snowpark Python application that needs to connect to an external REST API to enrich data during a transformation. The API requires authentication using an API key stored securely. Which of the following approaches is the MOST secure and recommended way to manage the API key within the Snowpark environment?

• A. Encrypt the API key using a third-party encryption library and store it in a Snowflake table.
• B. Store the API key in a Snowflake Secret Object and retrieve it within the Snowpark Python code using the function.
• C. Store the API key in a secure vault outside of Snowflake and retrieve it using a custom Snowflake external function.
• D. Hardcode the API key directly into the Snowpark Python code.
• E. Store the API key as an environment variable within the Snowflake session.

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. Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.
• B. 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.
• C. Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.
• D. 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.
• 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.

You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of

• A. Avoid using altogether because it can introduce overhead and is not always beneficial.
• B. Cache the final DataFrame only after all feature engineering steps are completed.
• C. Cache the initial raw data DataFrame before applying any transformations.
• D. Identify DataFrames that are reused multiple times and cache them using after the transformations that generate them.
• E. Cache each intermediate DataFrame after each individual transformation step, even if the DataFrame is only used once.