SPS-C01 Exam Dumps | Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial

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Question 109/151

Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A. Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.

B. Use to explicitly declare the Pandas dependency.

C. Change 'FloatType' to 'DoubleType' as it provides more precision.

D. Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

E. Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.

Correct Answer: A,D,E

Clustering by 'ticker_symbol' significantly optimizes the 'groupBy' operation by organizing data physically for efficient retrieval. Caching the result before writing to a table is crucial if the intermediate result 'result' will be used multiple times, avoiding redundant computations. Repartitioning by 'ticker_symbol' before grouping helps distribute data evenly, improving parallelism and locality. While Pandas is generally available, explicitly declaring it might be necessary in certain isolated environments, so B can be considered a good practice, but not as impactful. Changing FloatType to DoubleType (C) will not lead to performnace improvement. It only changes precision level which is not required here.

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