SPS-C01 Exam Dumps | You are tasked with optimizing the performance of a Snowpark Python application that performs complex

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Question 116/151

You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A. Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.

B. Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.

C. Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.

D. Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.

E. Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.

Correct Answer: C,D,E

Repartitioning allows for improved parallelism and reduces data skew, especially when the initial data distribution is uneven. Avoiding Python UDFs improves performance because they execute outside of Snowflake's optimized engine. Pushing down transformations and leveraging stored procedures minimizes data transfer between Snowpark and Snowflake, and leverages Snowflake's processing capabilities. Increasing warehouse size or enabling auto-scaling might help, but addressing data skew and UDF overhead will likely provide more significant performance gains.

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