DSA-C03 Exam Dumps | You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model.

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Snowflake.DSA-C03.v2026-01-08.q143
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Question 89/143

You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

Correct Answer: E

Option E is correct. It uses "F.avg' for calculating the average, selects only the required columns ('customer_id', 'average_transaction_amount_last_7_days') in and then sets on to ensure the feature group is fully materialized before proceeding. 'blocking-True' is important for production pipelines to avoid race conditions.

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