SPS-C01 Exam Dumps | You have a Snowpark DataFrame named 'customer df containing customer data, including sensitive information

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Question 41/151

You have a Snowpark DataFrame named 'customer df containing customer data, including sensitive information like credit card numbers in a column named 'credit card'. You need to persist this data to a Snowflake table named 'secure_customers'. What is the MOST secure and efficient way to achieve this, ensuring that the 'credit card' column is never exposed in plain text during the persistence process and also optimized for subsequent analytical queries?

A. Persist 'customer_df directly to 'secure_customers' using after dropping the 'credit_card' column using 'df.drop('credit_card')'.

B. Use a UDF to encrypt the 'credit_card' column before persisting the DataFrame to 'secure_customers' using

C. Create a Snowpark DataFrame that uses a Secure View to only select the required columns excluding credit_card, and persist that to 'secure_customers' using

D. Persist the 'customer_df to a temporary table using 'df.write.mode('overwrite').save_as_table('temp_customers')'. Then, create a new table 'secure_customers' from 'temp_customers' excluding the 'credit_card' column.

E. Create a masking policy in SnoMlake and apply it to the 'credit_card' column in the'secure_customers' table after persisting the 'customer_df using

Correct Answer: E

Applying a masking policy to the column AFTER persisting ensures that the data is protected at rest and dynamically masked based on the user's role/permissions. This approach minimizes the risk of exposing sensitive data during the transformation and persistence process. Using UDF encryption adds complexity and potential performance overhead. Dropping the column loses the data, and the temporary table approach exposes the unencrypted data temporarily.

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