COF-C03 Question 59
Single answerSnowpark OptimizedA data science team runs Python-based Snowpark workloads to train and score machine learning models directly in Snowflake. Their jobs use large in-memory data structures and frequently fail or run slowly on standard warehouses due to memory pressure. The team wants to improve performance for these memory-intensive Snowpark jobs while keeping the solution within Snowflake. Which action should the Snowflake administrator take?
- A
Create and use a Snowpark-optimized warehouse for the Python Snowpark workloads
- B
Enable Query Acceleration Service on the existing standard warehouse to provide more memory to Python processes
- C
Convert the workload to a serverless task so Snowflake automatically allocates GPU resources for model training
- D
Increase the MAX_CONCURRENCY_LEVEL on the existing warehouse so each Snowpark job gets more memory
Show answer and explanation
Correct answer: A
Explanation
Snowpark-optimized warehouses are the best fit when Snowpark workloads, especially Python-based data science and machine learning jobs, require more memory than standard warehouses typically provide. This is a practical configuration choice for in-Snowflake execution of memory-intensive code. By contrast, Query Acceleration Service targets eligible SQL query acceleration, not Snowpark runtime memory needs. Adjusting concurrency settings does not increase memory per workload, and serverless tasks are not a substitute for selecting the correct warehouse type. Snowflake documentation describes Snowpark-optimized warehouses as specialized warehouses for memory-intensive Snowpark workloads and recommends them for scenarios involving large in-memory processing.
- A. Correct.
Correct. Snowpark-optimized warehouses are designed for workloads such as Snowpark Python that need more memory per node and are appropriate for memory-intensive data engineering and machine learning use cases executed within Snowflake. This directly addresses the stated issue of large in-memory processing causing failures or poor performance on standard warehouses.
- B. Incorrect.
Incorrect. Query Acceleration Service is intended to accelerate parts of eligible SQL queries by offloading portions of scan and filter processing. It does not change a warehouse into a high-memory environment for Snowpark Python runtimes and does not solve memory pressure in Python-based Snowpark jobs.
- C. Incorrect.
Incorrect. Serverless tasks do not provide a mechanism to automatically assign GPU resources for Snowpark model training in this scenario. The key requirement is more memory for Snowpark execution, which is what Snowpark-optimized warehouses are intended to provide.
- D. Incorrect.
Incorrect. MAX_CONCURRENCY_LEVEL controls how many statements can run concurrently on a warehouse before queuing behavior changes. Raising it generally increases concurrency, not memory available to each individual Snowpark job. In fact, higher concurrency can increase resource contention rather than relieve memory pressure.