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DP-203 Practice Question: A company uses Azure Synapse Analytics dedicated…
A company uses Azure Synapse Analytics dedicated SQL pool. They notice that queries against a large fact table are running slower over time. The table is hash-distributed on a date key and has a clustered columnstore index. Which action should you take to improve query performance?
⚠ Common exam trap
A common mix-up: candidates assume performance degradation is always due to data skew or distribution choice, overlooking the common real-world issue of columnstore index fragmentation from ongoing DML operations.
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
✓
Rebuild the clustered columnstore index.
Over time, columnstore indexes can become fragmented due to insert, update, and delete operations, leading to compressed row groups that are not optimally sized or have deleted records. Rebuilding the clustered columnstore index reorganizes the data into fully compressed row groups, removes deleted rows, and restores the high compression and segment elimination that columnstore indexes rely on for fast query performance.
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 non-clustered index on frequently filtered columns.
Why it's wrong here
Columnstore tables are optimized for scans; adding non-clustered indexes can help point lookups but not scan-heavy queries.
- ✗
Change the distribution column to a column with higher cardinality.
Why it's wrong here
The date key already has high cardinality; changing distribution key would not solve fragmentation and would require significant rework.
- ✗
Change the distribution to round-robin.
Why it's wrong here
Round-robin distribution distributes rows evenly but increases data movement during joins, potentially worsening performance.
- ✓
Rebuild the clustered columnstore index.
Why this is correct
Rebuilding the columnstore index improves compression, removes deleted rows, and reorganizes rowgroups, enhancing scan performance.
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