ARA-C01 Question 397
Single answerSnowpark-optimized warehousesA data science team runs a nightly Snowpark Python pipeline that trains and scores several machine learning models entirely inside Snowflake. The job performs heavy in-memory processing in Python UDFs and stored procedures, and it regularly fails or runs much longer when executed on standard warehouses, even after increasing warehouse size. The architect needs to improve runtime consistency for these Snowpark workloads while keeping SQL-based ELT jobs on cost-efficient compute. Which approach should the architect recommend?
- A
Move the Snowpark Python pipeline to a Snowpark-optimized warehouse, and keep standard SQL ELT workloads on separate standard warehouses.
- B
Enable Query Acceleration Service on the existing standard warehouse so Python UDF memory limits are increased for the Snowpark pipeline.
- C
Convert the nightly pipeline to run on a serverless task, because serverless compute automatically provides the same memory profile as Snowpark-optimized warehouses.
- D
Use a multi-cluster standard warehouse for the Snowpark Python pipeline, because additional clusters primarily increase memory available to each Python process.
Show answer and explanation
Correct answer: A
Explanation
The best recommendation is to place the memory-intensive Snowpark Python pipeline on a Snowpark-optimized warehouse and keep conventional SQL ELT on standard warehouses. Snowpark-optimized warehouses are intended for workloads such as machine learning, Python UDFs, and stored procedures that need more memory per node than standard warehouses provide. In practice, this is the right architectural choice when a Snowpark workload remains unstable or slow on standard warehouses even after resizing. By contrast, multi-cluster warehouses address concurrency, not per-process memory, and Query Acceleration Service is not a substitute for the memory profile needed by Snowpark Python execution. Snowflake documentation and best practices distinguish between standard warehouses for general SQL processing and Snowpark-optimized warehouses for memory-intensive Snowpark workloads.
- A. Correct.
Correct. Snowpark-optimized warehouses are designed for workloads that require more memory per node, such as Snowpark Python jobs, Python UDFs, and stored procedures with significant in-memory processing. They are intended for cases where simply increasing the size of a standard warehouse does not address memory pressure efficiently. Keeping SQL ELT on standard warehouses is also a best practice, because Snowpark-optimized warehouses are specialized and generally should be used for the workloads that benefit from their memory characteristics.
- B. Incorrect.
Incorrect. Query Acceleration Service can help accelerate certain query processing patterns, but it does not change the memory model for Python execution environments in a way that substitutes for Snowpark-optimized warehouses. A common misconception is to treat QAS as a general-purpose performance fix for all workloads, including memory-intensive Snowpark Python execution.
- C. Incorrect.
Incorrect. Serverless tasks abstract warehouse management, but they are not a drop-in replacement for the specific higher-memory compute profile provided by Snowpark-optimized warehouses. This option confuses serverless operational convenience with workload-specific compute optimization for memory-intensive Snowpark code.
- D. Incorrect.
Incorrect. Multi-cluster warehouses are primarily used to improve concurrency by adding clusters, not to increase the memory available to an individual Python process or a single Snowpark job. This is a common misunderstanding: more clusters help handle more simultaneous workloads, but they do not solve per-node memory constraints for one memory-intensive pipeline.