SPS-C01 Exam Dumps | You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability

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Question 138/151

You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?

A. Directly translate the Pandas 'apply' operation to a Snowpark 'apply' operation, assuming that Snowpark's implementation is automatically optimized for distributed execution.

B. Rewrite the custom function as a vectorized operation using Snowpark DataFrame functions and expressions, avoiding row-by-row processing.

C. Create a Snowpark User-Defined Function (UDF) using Python and apply it to the DataFrame using the 'select method, leveraging Snowflake's distributed execution capabilities.

D. Use a stored procedure to execute the pandas 'apply' row by row on the data from snowflake table.

E. Utilize Snowpark's Pandas API to seamlessly execute the Pandas code within the Snowflake environment with minimal modifications.

Correct Answer: B,C

Vectorized operations in Snowpark provide the best performance by leveraging Snowflake's distributed processing. Creating a UDF allows you to push the computation to the Snowflake engine, avoiding the need to transfer large amounts of data to the Python environment. Direct translation to Snowpark 'apply' is not available as Snowpark 'apply' is significantly different, pandas code requires explicit data copying from and to snowflake. Stored procedures do not leverage the parallel processing capabilities of Snowflake as effectively as UDFs or vectorized operations. Pandas API is not the recommended way as UDF or vectorized operation.

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