COF-C03 Practice Question: Snowflake AI Data Cloud Features and Architecture
A Snowflake administrator is configuring a new virtual warehouse to support a data science team that runs occasional, complex queries on large datasets. The team requires fast performance and minimal latency. Which TWO warehouse configuration settings should the administrator consider to optimize performance for this workload? (Choose two.)
⚠ Common exam trap
It's easy for candidates to confuse concurrency scaling (multi-cluster) with performance scaling for individual queries, which is achieved through larger warehouse sizes or QAS.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable the Query Acceleration Service (QAS) for the warehouse.
For occasional complex queries on large datasets, increasing warehouse size provides more compute power for faster processing. Additionally, enabling the Query Acceleration Service (QAS) can offload eligible query parts to shared resources, further improving performance. Multi-cluster warehouses target concurrency, not single-query speed, and AUTO_SUSPEND settings affect cost, not performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the warehouse to use a larger number of clusters with scaling policy set to ECONOMY.
Why it's wrong here
The ECONOMY scaling policy is designed to minimize credit consumption by favoring fewer clusters, which can reduce performance during peak loads. For a data science team needing fast performance, a STANDARD scaling policy or a larger warehouse size is more appropriate. Multi-cluster settings do not improve single-query performance.
- ✗
Enable multi-cluster warehouse with a minimum cluster count of 2.
Why it's wrong here
Multi-cluster warehouses are designed for concurrency scaling, not for improving the performance of individual complex queries. They add clusters to handle many concurrent users, but a single complex query will not benefit from multiple clusters. For occasional complex queries, a larger single-cluster warehouse is more effective.
- ✗
Set AUTO_SUSPEND to a low value (e.g., 60 seconds) to minimize idle costs.
Why it's wrong here
While setting a low AUTO_SUSPEND reduces costs during idle periods, it does not directly optimize query performance. In fact, if the warehouse suspends too quickly, it may need to resume frequently, adding latency. For performance, the focus should be on compute resources, not suspension settings.
- ✓
Enable the Query Acceleration Service (QAS) for the warehouse.
Why this is correct
The Query Acceleration Service (QAS) offloads portions of query processing to shared compute resources, improving performance for queries with large scans and filters. It is beneficial for occasional, complex queries on large datasets, as it can reduce execution time without resizing the warehouse. Enabling QAS is a valid optimization for this scenario.
- ✓
Set the warehouse size to X-Large or larger.
Why this is correct
Increasing warehouse size provides more compute resources, which can significantly improve performance for complex queries on large datasets. Larger warehouses have more nodes and memory, allowing for faster processing and reduced query times. For occasional but resource-intensive workloads, a larger warehouse size is beneficial, even if it runs for short periods.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
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