Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

• A. Implementing row-level security policies on the sensitive data tables.
• B. Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
• C. Encrypting the stored procedure's code using AES encryption before deployment.
• D. Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
• E. Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.

You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

• A. Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
• B. Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.
• C. Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.
• D. Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
• E. Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.

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 '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.
• B. 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.
• 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 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.
• E. 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.

You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

• A. python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
• B. 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])
• C. python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)
• D. python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
• E. 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)

You are developing a Snowpark application to perform complex data transformations on a large dataset stored in Snowflake. You need to optimize the application's performance. Which of the following strategies are MOST effective for improving performance within Snowpark?

• A. Leveraging Snowpark's optimized functions and operations whenever possible, even if it requires rewriting some Python code.
• B. Using user-defined functions (UDFs) written in Python for all transformations, regardless of complexity.
• C. Taking advantage of Snowpark's lazy evaluation by chaining transformations together before triggering execution with an action like 'collect()' or 'write()'.
• D. Using the 'collect()' method frequently to retrieve small subsets of data to the client for processing.
• E. Materializing intermediate DataFrames aggressively to reduce memory usage.