You have a Snowpark DataFrame representing customer transactions. This DataFrame is used in multiple downstream operations within your Snowpark application. Which of the following strategies would be MOST effective for optimizing the performance of these downstream operations by materializing the results of the 'df DataFrame, and what considerations should be made regarding resource usage?

• A. Use 'df.checkpoint()' to truncate the DataFrame lineage. This will prevent re-computation in any downstream operations. Monitor the impact on storage costs.
• B. Create a temporary table using 'df.write.save_as_table('temp_transactions', temporary-True)'. This persists the DataFrame to Snowflake storage, reducing the need for repeated computations. Monitor the size of the temporary table and its impact on storage costs.
• C. Use to materialize the DataFrame in memory. This is the most efficient approach as it minimizes disk I/O. Consider the size of the DataFrame relative to available memory to avoid memory pressure.
• D. Using a local variable to store the DataFrame. This method is most suitable for materializing the results of the DataFrame.
• E. Write the DataFrame to a persistent Snowflake table using and then read it back into a new DataFrame. This ensures data persistence but may introduce overhead due to data serialization and deserialization. Only use this method if persistence is required beyond the session.

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

You have a Snowpark DataFrame named 'sales df that contains daily sales data'. You need to calculate the weekly sales for each product and store the results in a new DataFrame. The calculation of weekly sales involves a window function that is computationally expensive. To optimize performance, you decide to cache the DataFrame after applying the window function. However, after implementing the caching, you notice that the performance is not improved as expected. What could be the reason for this and how can you fix it?

• A. The window function is not cacheable. Window functions cannot be cached using 'cache_result()'.
• B. Snowflake automatically optimizes window function calculations, rendering explicit caching unnecessary.
• C. The DataFrame is being evicted from the cache due to memory pressure. Increase the warehouse size or reduce the data being processed.
• D. The call is placed before the window function. Move the call after applying the window function.
• E. The DataFrame is too small. Caching only benefits large DataFrames.

Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

• A. Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
• B. Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
• C. Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.
• D. Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
• E. Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.