COF-C03 Practice Question: Snowflake AI Data Cloud Features and Architecture
A data engineer creates a materialized view on a large sales fact table to improve performance for a frequently run aggregation query. After a few days, users report that the materialized view sometimes returns stale data compared to the base table. The engineer verifies that the base table is being updated continuously via Snowpipe. What is the most likely explanation for the stale results?
⚠ Common exam trap
The trap here is assuming that materialized views are updated in real-time with the base table, when in fact they are refreshed asynchronously.
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
✓
The materialized view is refreshed asynchronously, and there can be a delay between base table updates and the view reflecting those changes.
Materialized views in Snowflake are automatically maintained but refreshes are asynchronous, meaning there can be a delay before changes in the base table are reflected. This inherent lag explains why users might see stale data shortly after Snowpipe loads new records. The refresh process is managed by Snowflake and does not require manual intervention or interval settings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Materialized views are not supported on tables that are updated by Snowpipe; therefore, the view is not being refreshed.
Why it's wrong here
Materialized views can be created on tables that are loaded via Snowpipe. There is no restriction that prevents materialized views on such tables. The issue is not lack of support but the asynchronous nature of the refresh process. The view may become stale until the next automatic refresh occurs.
- ✓
The materialized view is refreshed asynchronously, and there can be a delay between base table updates and the view reflecting those changes.
Why this is correct
Materialized views in Snowflake are maintained automatically but asynchronously. When the base table changes, the materialized view is not updated immediately; instead, Snowflake schedules a refresh that may introduce a lag. This explains why users see stale data for a period after Snowpipe loads new rows.
- ✗
The materialized view was created without specifying a refresh interval, so it never refreshes automatically.
Why it's wrong here
Snowflake materialized views do not use a user-specified refresh interval. They are refreshed automatically by the service when the base table changes, with no manual interval configuration. The absence of a refresh interval does not cause the view to never refresh; the refresh is managed internally.
- ✗
Materialized views in Snowflake are automatically refreshed only when the base table changes, but Snowpipe loads do not trigger refreshes.
Why it's wrong here
Snowflake materialized views are refreshed automatically when the base table changes, but the refresh is asynchronous and may not be immediate. Snowpipe loads do trigger change tracking, so the statement that Snowpipe loads do not trigger refreshes is incorrect. The delay is due to refresh scheduling, not lack of trigger.
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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.