ARA-C01 Performance Optimization Practice Question
A data architect is analyzing a query that performs poorly due to excessive data shuffling during a large join operation. The query joins two large tables on a non-clustered key. Which Snowflake feature can help reduce data shuffling by co-locating related data?
⚠ Common exam trap
Candidates often confuse features that improve query performance with those that specifically reduce data shuffling; clustering directly influences data layout for 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
✓
Clustering the tables on the join key
Clustering the tables on the join key helps co-locate matching rows, reducing the need to shuffle data across nodes during a join. This can significantly improve performance for large joins. The other options address different performance aspects: Search Optimization for point lookups, Query Acceleration for scan-heavy queries, and multi-cluster warehouses for concurrency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Multi-cluster warehouse
Why it's wrong here
A multi-cluster warehouse adds compute resources to handle concurrency, but it does not change how data is distributed or joined. It cannot reduce shuffle because the underlying data organization remains the same. It addresses queuing and concurrency, not join performance.
- ✗
Search Optimization Service
Why it's wrong here
Search Optimization Service improves point lookup queries by maintaining a search access path, but it does not co-locate data for joins. It is designed for highly selective filters, not for reducing shuffle during large joins. Enabling it would not address the shuffle issue described.
- ✗
Query Acceleration Service
Why it's wrong here
Query Acceleration Service offloads portions of query processing to shared compute resources, which can speed up queries with large scans and filters, but it does not specifically reduce data shuffling for joins. It is more effective for queries with selective filters, not for join-heavy workloads.
- ✓
Clustering the tables on the join key
Why this is correct
Clustering a table on the join key physically sorts the data by that key, which can allow the optimizer to perform co-located joins or reduce data movement. When both tables are clustered on the join key, matching rows are more likely to reside on the same micro-partitions, minimizing shuffle during the join operation.
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Last reviewed September 2026 · checked against the official Snowflake exam blueprint
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