You are developing a Snowpark application in Python to process financial transactions. You're using a Snowpark DataFrame named 'transactions_df which contains sensitive financial data, including customer account numbers and transaction amounts. To comply with data privacy regulations, you need to mask the customer account numbers before performing any aggregations. The 'transactions_df DataFrame has a column named 'ACCOUNT NUMBER' (VARCHAR). You decide to use a User-Defined Function (UDF) to mask the account numbers using a cryptographic hashing algorithm. Which of the following approaches is the most secure and efficient way to define and use the UDF in Snowpark, ensuring the masking occurs within the Snowflake environment and minimizes the risk of exposing sensitive data?
Correct Answer: C
Option C is the most secure and efficient because it uses Snowflake's built-in 'SHA2 function within a SQL UDF. This approach keeps the sensitive data within the Snowflake environment, minimizing the risk of exposing the data during transfer to and from external systems. Snowflake's built-in functions are also optimized for performance. Option A is less secure because it uses Python's 'hashlib' , which may not be as robust as Snowflake's built-in cryptographic functions. It also potentially exposes data to the Python environment. Option B can be secure, but it requires more effort to manage dependencies and ensure compatibility. SQL UDFs are generally simpler and easier to maintain for this type of task. Option D is not secure because simple string manipulation is easily reversible and does not provide adequate data protection. Option E is highly insecure because it involves extracting the sensitive data from Snowflake and processing it in your application, which significantly increases the risk of data exposure.