COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data analyst runs a dashboard query that aggregates sales by region for the current month. The query has run successfully many times today, but the analyst notices it is returning results in under a second even though the underlying table is very large. The analyst has not changed the query or the data. Which Snowflake feature is most likely responsible for the fast response?
⚠ Common exam trap
The trap here is attributing fast repeated queries to warehouse size or clustering, when the persisted result cache is designed to return identical query results without any compute.
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
✓
Result caching has stored the query result, and the query is being served from the persisted result cache.
Result caching in Snowflake stores the complete output of a query for 24 hours. When the same query is re-run and the underlying data has not changed, Snowflake returns the cached result directly without using a warehouse. This yields sub-second response times even for large aggregations. Clustering, warehouse size, and local disk cache all affect query execution speed but do not store final results, so they cannot explain the instant response.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Result caching has stored the query result, and the query is being served from the persisted result cache.
Why this is correct
Snowflake's result cache stores the output of every query for 24 hours. If the same query is re-executed and the underlying data has not changed, Snowflake returns the cached result without re-running the query. This explains the sub-second response for a repeated aggregation on a large table. The cache is invalidated when the data changes or when certain session parameters differ, but in this scenario the analyst has not changed anything.
- ✗
The query is using the local disk cache on the virtual warehouse.
Why it's wrong here
The local disk cache stores data files that have been accessed by the warehouse, speeding up subsequent scans. However, it still requires the warehouse to execute the query and perform the aggregation. It does not store final query results. For a large aggregation, even with all data cached locally, the compute time would be more than sub-second. The result cache, which stores the final output, is the more likely explanation.
- ✗
The table is clustered on the region column, allowing partition pruning.
Why it's wrong here
Clustering can improve performance by pruning micro-partitions, but it does not produce sub-second results for a full aggregation over a large table unless the table is tiny or the clustering key perfectly matches the filter. The scenario mentions no clustering key and says the table is very large. Clustering reduces the amount of data scanned but still requires computation. It is not the primary reason for the extremely fast repeated query.
- ✗
The virtual warehouse is configured with a large multi-cluster size.
Why it's wrong here
A larger or multi-cluster warehouse increases compute concurrency and throughput, but it does not explain sub-second response for a query that scans a very large table. Even with many clusters, scanning terabytes of data takes time. The scenario says the query has run repeatedly without changes, which points to a caching mechanism rather than raw compute power. Warehouse sizing would improve performance but not to sub-second levels for a large aggregation.
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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
This COF-C03 practice question is part of Courseiva's free Snowflake certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the COF-C03 exam.