SPS-C01 Exam Dumps | You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations

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Question 79/151

You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Convert the DataFrame to a Pandas DataFrame before applying the UDF.

B. Increase the number of UDF worker threads within the UDF definition.

C. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.

D. Broadcast the DataFrame to all compute nodes before applying the UDF.

E. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.

Correct Answer: E

Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

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