DSA-C03 Exam Dumps | You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake

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Snowflake.DSA-C03.v2026-01-08.q143
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Question 110/143

You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E

Correct Answer: E

Option E is the only fully correct approach. It correctly groups by 'CUSTOMER_ID and finds the maximum transaction date. It calculates the Recency by using 'datediff, , and casting 'LAST_TRANSACTION_DATE' with Without the cast to , it is possible to run into error in 'datediff function. 'datediff function will cause issues when used on a timestamp. The 'recency_sdf dataframe will only have customer_id and recency.

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