COF-C03 Practice Question: Snowflake AI Data Cloud Features and Architecture
Which feature allows Snowflake to automatically utilize results from previous queries without consuming compute resources?
⚠ Common exam trap
Candidates frequently confuse the Query Result Cache with the Data Cache (Warehouse Cache). The Result Cache is global and requires no warehouse compute, while the Data Cache lives on the warehouse.
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
✓
Query Result Cache
The Query Result Cache stores the results of queries for 24 hours. If an identical query is executed and the underlying data has not changed, Snowflake returns the result from the cache immediately. This avoids the need to spin up or use warehouse compute resources, providing near-instant responses while significantly reducing costs. It is a critical component for optimizing dashboard performance and reducing redundant processing of static data in analytical environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Warehouse Caching
Why it's wrong here
Warehouse caching (Local Disk Cache) stores data from micro-partitions on the SSDs of the compute nodes. It accelerates repeat queries that process the same data, but it still requires the warehouse to be active and utilize compute resources to process the cached data blocks during the query execution.
- ✓
Query Result Cache
Why this is correct
The Query Result Cache is a persistent storage feature in the Cloud Services layer that holds the output of queries. Because it stores the finished result sets, the system can return the data without executing the query plan, which requires zero compute usage from the warehouse.
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Metadata Cache
Why it's wrong here
The Metadata Cache stores information about the data (such as row counts and partition ranges) to speed up query planning. While it improves performance by reducing the time taken to build an execution plan, it does not bypass the execution phase itself, which still requires compute resources.
- ✗
Micro-partition Pruning
Why it's wrong here
Pruning is an optimization technique where the engine ignores files that don't match query filters. While it saves compute cycles by reducing the data scanned, it is still an active query execution process that requires the warehouse to be running and consuming credits to perform the evaluation.
About these practice questions
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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.