SPS-C01 Exam Dumps | You are tasked with optimizing a Snowpark Python application that performs complex data transformations

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Snowflake.SPS-C01.v2026-08-07.q151
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Question 40/151

You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A. Increase the warehouse size to the largest possible option.

B. Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.

C. Use the function to sort the data before performing the transformations.

D. Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.

E. Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.

Correct Answer: B,E

Options B and E are effective techniques for mitigating data skew. Option B, allows you to explicitly redistribute the data based on a specific column or set of columns, ensuring a more even distribution across the warehouse nodes. Option E, using the 'BROADCAST' hint, is useful when joining a smaller DataFrame with a large DataFrame, as it broadcasts the smaller DataFrame to all nodes, preventing skew during the join operation. Option A, increasing the warehouse size, might provide more resources but doesn't address the underlying data skew issue directly. Option C, sorting the data, doesn't necessarily address data skew and might even worsen it in some cases. Option D, Snowflake's automatic clustering, helps with data locality for queries but doesn't directly address data skew within the Snowpark application during transformations.

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