ARA-C01 Performance Optimization Practice Question
An architect is designing a dashboard that runs a query joining a 5 TB fact table to a small 2 GB dimension table. The fact table is not clustered, and the dimension table is updated hourly. The query filters on a high-cardinality column in the fact table and joins on a low-cardinality key. The architect wants to minimize query latency without increasing warehouse size. Which approach is most effective?
⚠ Common exam trap
The trap here is assuming that Search Optimization Service can replace clustering for all selective queries, when it is actually optimized for point lookups and may not help with range scans or joins.
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 fact table's high-cardinality filter column.
The query's main cost is scanning the large fact table. Clustering on the high-cardinality filter column enables partition pruning, reducing I/O. The small dimension table does not warrant special handling. Search Optimization is for point lookups, materializing adds overhead, and resizing does not address the root cause of excessive data scanning.
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 fact table's high-cardinality filter column.
Why this is correct
Clustering the fact table on the high-cardinality filter column improves partition pruning, so the query reads fewer micro-partitions. This reduces I/O and speeds up the join without resizing the warehouse. Since the dimension is small, it can be broadcast or cached, so the main bottleneck is scanning the large fact table. Clustering directly addresses that bottleneck.
- ✗
Enable the Search Optimization Service on the fact table.
Why it's wrong here
Search Optimization Service accelerates point lookups and selective queries using an inverted index, but it is not designed for large scans or joins. The query filters on a high-cardinality column but likely still scans many rows, so the search index would not provide the same pruning benefit as clustering. It also adds maintenance overhead and cost, making it less suitable for this analytical join scenario.
- ✗
Materialize the join as a new table and refresh it hourly.
Why it's wrong here
Materializing the join would require storage and compute to refresh hourly, and it adds complexity. The dimension table is small, so the join itself is not the bottleneck; the large fact table scan is. Creating a new table does not reduce the scan cost unless it is also clustered or filtered, and it introduces staleness and maintenance overhead that may not be justified.
- ✗
Increase the warehouse size to add more compute nodes.
Why it's wrong here
Scaling up the warehouse adds compute resources, which can help with CPU-bound operations, but the primary issue is I/O from scanning a large unclustered table. A larger warehouse may not reduce the amount of data read, so the performance gain could be limited and costly. The architect explicitly wants to avoid increasing warehouse size, and clustering is a more targeted solution.
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Last reviewed September 2026 · checked against the official Snowflake exam blueprint
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