ARA-C01 Question 396
Single answerSnowpark-optimized warehousesA data science team runs a nightly Snowpark Python pipeline that performs feature engineering and model scoring on hundreds of millions of rows. The code relies heavily on Python user-defined functions and Snowpark DataFrame transformations. Although the SQL portions of the pipeline perform well, the job spends most of its time in Python execution and frequently runs out of memory on a standard warehouse, forcing engineers to increase warehouse size repeatedly. The architect wants to improve performance for this workload without redesigning the application logic. Which action should the architect take?
- A
Run the pipeline on a Snowpark-optimized warehouse sized for the workload, because it provides more memory per node for memory-intensive Snowpark execution
- B
Enable Query Acceleration Service on the existing standard warehouse, because it accelerates Python UDF execution and reduces interpreter memory pressure
- C
Convert the warehouse to a multi-cluster warehouse with additional clusters, because extra clusters increase the memory available to each Python process in a single query
- D
Replace the warehouse with a serverless task, because serverless compute automatically uses Snowpark-optimized resources for Python workloads
Show answer and explanation
Correct answer: A
Explanation
Snowpark-optimized warehouses are a best-practice choice when Snowpark workloads, particularly Python-based transformations, UDFs, and stored procedures, are constrained by memory on standard warehouses. They provide more memory per node than standard warehouses, which helps with memory-intensive execution patterns common in data science and machine learning pipelines. In contrast, multi-cluster warehouses address concurrency, not the performance of a single memory-heavy job. Query Acceleration Service is not intended to fix Python runtime memory bottlenecks. When a scenario explicitly mentions Snowpark Python execution, memory exhaustion, and a desire to improve performance without major code changes, the architect should recognize Snowpark-optimized warehouses as the targeted solution. This aligns with Snowflake documentation describing Snowpark-optimized warehouses as purpose-built for memory-intensive Snowpark workloads.
- A. Correct.
Correct. Snowpark-optimized warehouses are designed for memory-intensive workloads such as Snowpark, especially Python-based processing, UDFs, and stored procedures that can require more memory than standard virtual warehouses provide. In this scenario, the bottleneck is Python execution and memory pressure rather than general SQL throughput, so moving to a Snowpark-optimized warehouse is the most appropriate architectural change without rewriting the pipeline.
- B. Incorrect.
Incorrect. Query Acceleration Service is intended to improve the performance of eligible query processing by offloading portions of scan and filter work, but it is not a mechanism for providing more memory to Python runtimes or accelerating Python UDF interpreter execution. This option reflects a common misconception that all performance features help all workload types equally.
- C. Incorrect.
Incorrect. Multi-cluster warehouses primarily improve concurrency by adding clusters to handle more simultaneously running queries. They do not increase the resources available to a single query running on one cluster, nor do they solve per-query Python memory constraints. A single long-running Snowpark job with memory issues typically does not benefit from multi-cluster scaling.
- D. Incorrect.
Incorrect. Serverless tasks provide managed compute for task execution, but they are not simply a substitute for selecting Snowpark-optimized warehouse characteristics for a memory-heavy Snowpark pipeline. This option confuses orchestration choice with compute-profile selection. The key issue here is the compute environment required by the job, not whether the pipeline is invoked by a task.