Exam SPS-C01 Topic 1 Question 353 Discussion

Actual exam question for Snowflake's SPS-C01 exam
Question #: 353
Topic #: 1

You have a Snowpark Python application that reads data from a Snowflake table, performs a complex transformation using a User- Defined Table Function (UDTF), and then writes the transformed data back to a new Snowflake table. The UDTF is defined as follows:

You need to optimize the performance of this application. Which of the following strategies would be MOST effective in reducing the execution time of the UDTF?

A. Increase the warehouse size to the largest available option before running the Snowpark application. B. Reduce the number of rows in the input table by applying a filter before calling the UDTF. C. Use a vectorized UDTF instead of a standard UDTF, processing data in batches. (Assume 'vectorized=True' is a valid parameter for 'udf .) D. Replace the UDTF with a standard User-Defined Function (UDF) as UDFs are inherently faster. E. Cache the input DataFrame before applying the UDTF using 'df.cache()'.

Suggested Answer: C Vote an answer

Vectorized UDTFs process data in batches, which can significantly improve performance compared to processing each row individually. This is especially true for complex transformations. Increasing warehouse size (A) can help but might not be as efficient as vectorization. Reducing input data (B) is always a good practice, but vectorization provides a more direct performance boost to the UDTF execution. Standard UDFs (D) are not generally faster than UDTFs, especially when dealing with table transformations. Caching (E) can help if the DataFrame is reused multiple times, but it doesn't directly optimize the UDTF's performance.

by Vicky at Feb 24, 2026, 12:00 AM

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