DEA-C02 Performance Optimization Practice Question
What is the primary benefit of using a 'Materialized View' over a standard view in Snowflake?
⚠ Common exam trap
Many students confuse materialized views with result cache or standard views, forgetting that materialized views persistently store pre-computed results on disk to save compute costs.
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
✓
It reduces compute costs by pre-computing query results.
Materialized views store the pre-computed results of a query, which avoids the overhead of re-calculating the results during each execution. This is extremely beneficial for queries that are complex, resource-intensive, and executed frequently. By contrast, a standard view computes its output every time it is called, consuming compute resources and potentially causing latency for end-users, whereas materialized views provide near-instant access to the computed dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It supports real-time data streaming updates.
Why it's wrong here
Materialized views are not designed for real-time streaming updates. They are background-maintained, meaning there is a slight lag between the base table updates and the materialized view updates. They are optimized for read-heavy workloads, not for handling high-frequency, real-time streaming data ingestion.
- ✗
It automatically updates based on all underlying table changes.
Why it's wrong here
While Snowflake does maintain them, this maintenance is limited to specific DML operations. If the view definition is too complex, it may not be supported or could become inefficient. They are not a magic solution for all complex queries, and they impose their own maintenance credit cost.
- ✓
It reduces compute costs by pre-computing query results.
Why this is correct
By storing the pre-computed output of a query, materialized views save compute resources for repetitive, complex queries. This reduces the need to run the underlying logic every time the view is accessed, significantly improving read performance and reducing the overall credit consumption for heavy analytical workloads.
- ✗
It is the only way to join two tables in Snowflake.
Why it's wrong here
Standard SQL joins are fully supported in Snowflake for any query, whether using standard views, tables, or subqueries. Materialized views are an optimization tool, not a functional requirement for performing joins. They are used exclusively to improve the performance of specific, expensive data retrieval patterns.
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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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