DSA-C03 Exam Dumps | You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame

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Question 7/143

You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E

Correct Answer: B

Option B is the most efficient solution because it performs both the log transformation and the average calculation entirely within Snowflake using Snowpark functions. This avoids the overhead of transferring the data to the client side. It uses F.logl p() to apply the log transformation to the 'transaction\_amount' column, handling potential zero values gracefully. It groups by 'customer\_id' and uses F.mean() to calculate the average of the transformed transaction amounts.

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