COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A data engineer is optimizing a transformation pipeline and wants to reduce compute cost and improve performance for queries that repeatedly scan the same large table with different filters. Which TWO Snowflake features or techniques directly support this goal? (Choose two.)
⚠ Common exam trap
Many candidates confuse cost-control or storage settings with genuine query-performance features, since parameters like timeouts and table types sound optimization-adjacent but do not reduce scanned data.
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
✓
Create a materialized view that pre-aggregates the filtered results.
Clustering and materialized views both attack the cost of repeated scans. Clustering reduces the micro-partitions read by improving pruning on filtered columns, while materialized views precompute and maintain aggregated results so recurring queries avoid touching the base table. Together they cut compute for the described pattern, whereas timeout settings, cache disabling, and transient storage do not improve scan efficiency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a materialized view that pre-aggregates the filtered results.
Why this is correct
A materialized view stores precomputed results and is automatically maintained by Snowflake, so repeated queries against the same aggregated data avoid rescanning the base table. Snowflake can also rewrite eligible queries to use the materialized view. This reduces compute for recurring aggregation patterns, directly supporting the stated optimization goal.
- ✓
Define a clustering key on the columns most frequently used in WHERE predicates.
Why this is correct
Clustering reorganizes micro-partitions so that frequently filtered columns have better overlap and min-max pruning. When the same large table is scanned repeatedly with different predicates on those columns, clustering reduces the number of micro-partitions read per query, lowering compute and improving latency. This directly serves the goal of repeated scans with varying filters.
- ✗
Disable the result cache at the account level to force fresh execution.
Why it's wrong here
Disabling the result cache removes a zero-cost reuse path for identical queries, which would increase compute rather than reduce it. The result cache returns prior results when the query text and underlying data are unchanged, saving credits. Turning it off contradicts the goal of lowering compute for repeated access patterns and is counterproductive here.
- ✗
Convert the table to a transient table to avoid fail-safe storage charges.
Why it's wrong here
Transient tables affect data retention and fail-safe storage costs, not query performance or compute consumption. Scanning behavior and micro-partition pruning are unchanged. While transient tables can lower storage expense, they do nothing to reduce the compute cost of repeatedly scanning a large table with different filters, so this choice misses the stated objective.
- ✗
Increase the STATEMENT_TIMEOUT_IN_SECONDS parameter for the session.
Why it's wrong here
STATEMENT_TIMEOUT_IN_SECONDS controls how long a statement may run before being canceled; it does not reduce scanned data or compute cost. Raising it merely allows long queries to continue, which could increase cost. This parameter is a guardrail, not an optimization technique, and has no effect on the efficiency of repeated scans.
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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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