SPS-C01 Exam Dumps | You have developed a Snowpark application that processes a large volume of customer interaction data'.

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Question 144/151

You have developed a Snowpark application that processes a large volume of customer interaction data'. The application uses a vectorized UDF to classify the sentiment of text-based interactions. Initial tests show the application is performing slowly. Which of the following strategies would be MOST effective for optimizing the performance of sentiment analysis using a vectorized UDF?

A. Increase the number of cores allocated to the virtual warehouse.

B. Implement mini-batching within the vectorized UDF to further optimize processing.

C. Ensure the input DataFrame is sorted by the interaction date before applying the UDF.

D. Optimize the vectorized UDF code itself by reducing complex computations, memory allocations, and unnecessary function calls within the UDF function (e.g., using optimized libraries, avoiding unnecessary object creation). Also consider upgrading your pandas, scikit-learn, or transformer libraries.

E. Convert the vectorized UDF to a SQL UDTF

Correct Answer: D

Optimizing the vectorized UDF code itself offers the most direct and significant performance gains for sentiment analysis. By streamlining computations, reducing memory allocations, and avoiding unnecessary function calls within the UDF's vectorized function, the processing time for each batch of data can be substantially reduced. Mini-batching (B) is already implicit with vectorization with pandas series/dataframe. Increaseing Number of cores allocated to the virtual warehouse can provide some help but it depends on other factor also. Other oprions may not have impact

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