COF-C03 Practice Question: Performance Optimization, Querying, and Transformation
A user runs a query that filters on a column with a high cardinality and the table is not clustered. The query scans a large number of micro-partitions. Which action would most directly reduce the number of micro-partitions scanned?
⚠ Common exam trap
The trap here is thinking that a bigger warehouse reduces data scanned; it only makes scanning faster, not narrower.
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
✓
Add a clustering key on the filtered column.
Clustering keys physically sort data so that similar values are co-located in micro-partitions. This enables the optimizer to prune partitions based on filter predicates, directly reducing the number of micro-partitions scanned. Larger warehouses or caching do not change the volume of data scanned for the initial query.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a clustering key on the filtered column.
Why this is correct
Clustering keys reorganize the micro-partitions so that data with similar values is stored together. When a query filters on the clustered column, the optimizer can use the clustering metadata to prune micro-partitions that do not contain the filter value. This directly reduces the number of micro-partitions scanned, improving performance for high-cardinality columns.
- ✗
Use a larger warehouse with multi-cluster scaling.
Why it's wrong here
Multi-cluster warehouses add concurrency, allowing more queries to run simultaneously, but they do not reduce the data scanned per query. Each query still reads the same micro-partitions. This option addresses workload concurrency, not partition pruning. Therefore it does not directly reduce the number of micro-partitions scanned for a single query.
- ✗
Increase the size of the virtual warehouse.
Why it's wrong here
Increasing warehouse size adds more compute resources, which can speed up the scan of micro-partitions, but it does not reduce the number of micro-partitions that must be read. The query will still scan the same volume of data; only the processing speed increases. This is a scaling-out approach, not a pruning optimization.
- ✗
Enable result caching for the session.
Why it's wrong here
Result caching stores the output of a query for reuse, but it does not affect the number of micro-partitions scanned for a new query. If the query is run again with the same text and unchanged data, it can return cached results, but the initial execution still scans all necessary partitions. It does not address the pruning issue.
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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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