SPS-C01 Exam Dumps | You have a Snowpark Python application that performs complex data transformations and machine learning

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Question 76/151

You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?

A. Use the 'sproc' decorator to define user-defined functions (UDFs) directly within Snowflake, leveraging the platform's optimized execution engine for specific computations.

B. Cache intermediate Snowpark DataFrames using to avoid recomputation of common data transformations across multiple training iterations.

C. Leverage external functions (IJDFs) to offload computationally intensive operations to specialized hardware outside of Snowflake.

D. Scale the virtual warehouse UP to an XLARGE or larger. This provides more computational resources.

E. Implement data skipping and filtering strategies to reduce the amount of data read during feature extraction and model training. Pre-aggregate when possible.

Correct Answer: B,E

Caching intermediate DataFrames (Option B) avoids redundant computations, significantly speeding up iterative algorithms. Filtering and data skipping (Option E) reduce the amount of data processed, which is especially beneficial with large feature sets. While sprocs (Option A) offer performance benefits for certain IJDFs, the impact on overall model training performance might be less significant compared to caching and data reduction. External functions (Option C) may introduce network latency, outweighing the potential benefits, unless carefully optimized for data transfer. Scaling up (Option D) might help, but is not the most likely to improve performance, given that the warehouse is already at a LARGE size. Focusing on more efficient data handling is better in most cases.

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