SPS-C01 Exam Dumps | You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes

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Question 118/151

You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes timestamp, sensor ID, and sensor reading. Your initial implementation uses a regular Python UDF to calculate the moving average for each sensor. However, the processing time is significantly slow due to the large volume of data'. Which of the following strategies would be MOST effective in improving the performance of this calculation using vectorization?

A. Replace the Python UDF with a SQL UDF as SQL UDFs are inherently faster.

B. Convert the existing Python UDF into a vectorized UDF using the '@vectorized' decorator, ensuring the input and output are Pandas Series.

C. Increase the warehouse size without modifying the UDF code.

D. Rewrite the calculation logic using Snowpark's built-in aggregation functions instead of a UDF.

E. Convert the Python UDF to a Java UDF.

Correct Answer: B

Converting the Python UDF to a vectorized UDF allows it to process data in batches (as Pandas Series), which significantly reduces the overhead of transferring data between Snowflake and the UDE While increasing warehouse size (C) can provide some performance gain, vectorization (B) directly addresses the inefficiency of processing individual rows. Using built-in aggregation (D) is also a good option if feasible, but if a custom moving average calculation is required, vectorized UDF is the best fit. SQL UDFs aren't always faster and don't inherently vectorize. Java UDFs may provide some improvement but are more complex to implement than vectorized Python UDFs.

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