Exam SPS-C01 Topic 1 Question 183 Discussion

Actual exam question for Snowflake's SPS-C01 exam
Question #: 183
Topic #: 1

You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A. Switch to a larger Snowflake warehouse size and increase the parameter to ' True'. B. Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application. C. Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures. D. Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk. E. Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.

Suggested Answer: B,E Vote an answer

Reducing the batch size reduces the memory footprint of each batch. Using optimized data types reduces memory usage for each column. Profiling helps identify and fix memory leaks in the code. Increasing warehouse size helps improve performance. MEMORY_OPTIMIZED server type will allocate higher memory. Optimizing UDFs can improve performance significantly. Using Native Execution will utilize the native execution engine of Snowflake for faster query processing. Option A utilizes native execution (if supported) but doesn't address memory constraints. Option C rewriting model training code in SQL might not be feasible. Option D switching to smaller warehouse exacerbates the memory problem. Reducing batch size, memory profiling and optimized UDF/Data type are key. Using a memory-optimized warehouse type is also important.

by Meredith at Aug 16, 2026, 09:50 PM

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