SPS-C01 Exam Dumps | You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex

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Question 11/151

You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

A. Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.

B. Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.

C. Increase the warehouse size to the largest possible value before executing the stored procedure.

D. Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.

E. Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.

Correct Answer: B,D

Options B and D are the most effective. Vectorized operations (B) significantly speed up Pandas calculations. Performing transformations within Snowflake (D) avoids unnecessary data transfer between Snowpark and Pandas, reducing the bottleneck. A is useful, but secondary. C only affects Snowflake side processing, but it may help. E would be useful, but not as helpful as pushing as much as possible down to Snowflake processing.

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