SPS-C01 Exam Dumps | You are developing a Snowpark application that performs complex data transformations on a large dataset

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Question 78/151

You are developing a Snowpark application that performs complex data transformations on a large dataset using a UDF written in Scala.
After deploying the application, you observe that the performance is significantly slower than expected. Analyzing the query history in Snowflake, you identify that the UDF execution time is unusually high. Which of the following actions would be MOST effective in improving the performance of the UDF, considering Snowpark's execution context and Snowflake's query processing?

A. Increase the warehouse size to improve overall query processing capacity, even if the UDF code itself remains unchanged.

B. Modify the Scala UDF to leverage Snowpark's vectorized UDF functionality (using 'VectorizedUDF) to process data in batches instead of row-by-row.

C. Rewrite the Scala UDF using SQL stored procedure for better performance.

D. Increase the amount of memory allocated to the Snowpark session in the application code.

E. Change the UDF definition to return smaller data types if applicable.

Correct Answer: B,E

Using vectorized UDFs allows processing data in batches, significantly reducing overhead. Returning smaller datatypes optimizes I/O and memory usage. While increasing the warehouse size might offer some improvement, it doesn't directly address the UDF's inefficiency. Snowpark session memory is more relevant for the driver program and less so for the execution of the UDF within Snowflake's environment. SQL Stored procedures are useful but for functions already supported in SQL; vectorized UDFs provide a path fomard for scala code.

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