SPS-C01 Exam Dumps | You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame

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Question 95/151

You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)

A. Eagerly evaluating all DataFrame transformations using 'df.collect()' after each transformation to materialize the intermediate results.

B. Leveraging Snowflake's caching mechanisms by using the 'CACHE RESULT clause after complex or frequently used queries.

C. Using pushdown optimization by writing UDFs in Scala and ensuring filter operations are applied as early as possible in the data processing pipeline.

D. Using to explicitly define the order in which DataFrames should be processed.

E. Using on intermediate DataFrames that are reused multiple times in subsequent transformations.

Correct Answer: C,E

Options C and E are correct. Option C, pushdown optimization by ensuring filter operations are applied as early as possible, is a key optimization technique. UDFs written in Scala can also be optimized by the compiler and Snowflake's engine. Option E, using , is the correct way to cache intermediate DataFrames for reuse, preventing redundant computations. Option A is incorrect; eagerly evaluating DataFrames with 'collect()' defeats the purpose of lazy evaluation and can significantly degrade performance. Option B is not directly applicable to Snowpark DataFrame transformations; 'CACHE RESULT is primarily for SQL queries executed outside of Snowpark DataFrame operations. Option D, is not a valid function in Snowpark API.

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