NAS-C01 Exam Dumps | You are developing a Snowflake Native Application that leverages Snowflake's Snowpark API for data processing.

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Question 160/161

You are developing a Snowflake Native Application that leverages Snowflake's Snowpark API for data processing. The application performs a series of complex transformations on a DataFrame. You need to optimize the application's performance and minimize resource consumption. Which of the following strategies would be MOST effective in achieving this goal?

A. Use the method frequently to materialize intermediate DataFrames and inspect the data.

B. Rely on Snowpark's query optimizer to automatically optimize the entire data processing pipeline without any manual intervention.

C. Employ lazy evaluation by chaining together transformations on the DataFrame and only triggering execution when the final result is needed.

D. Explicitly specify the execution order of the transformations using 'cache()' on each intermediate DataFrame to force immediate materialization.

E. Break down the DataFrame processing into smaller steps and store the intermediate results in temporary tables within the application's sandbox database.

Correct Answer: C

Lazy evaluation (C) is the most effective strategy. By chaining transformations and only executing when the final result is needed, Snowpark's query optimizer can optimize the entire pipeline, reducing resource consumption. 'collect()' (A) materializes DataFrames unnecessarily, hindering optimization. While Snowpark does optimize queries (B), relying solely on it is insufficient. Explicit materialization (D) defeats the purpose of lazy evaluation. Storing intermediate results in temporary tables (E) adds overhead and complexity.

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