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DP-203 Practice Question: You have an Azure Synapse Analytics dedicated SQL…
You have an Azure Synapse Analytics dedicated SQL pool. You notice that some queries are taking longer than expected. After reviewing the query plans, you see that some queries are spilling to tempdb. What should you do to reduce tempdb spills?
⚠ Common exam trap
It's easy for candidates to confuse performance tuning techniques like indexing or partitioning with memory management, assuming any optimization will fix spills, when only increasing memory allocation (via resource class) directly addresses the root cause.
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
✓
Increase the resource class for the user executing the queries.
Tempdb spills occur when a query requires more memory than is allocated to it, forcing intermediate results to be written to disk. Increasing the resource class for the user executing the queries allocates more memory to that user's queries, reducing the likelihood of spills. This directly addresses the memory constraint that causes spills in a dedicated SQL pool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the resource class for the user executing the queries.
Why this is correct
A higher resource class grants the query more memory per distribution, so sort and hash operations fit in memory rather than spilling to tempdb. This directly addresses the memory pressure causing the spills observed in the query plans.
- ✗
Redistribute the tables using hash distribution.
Why it's wrong here
Hash redistribution changes data placement across distributions to reduce data movement, not the memory available per query, so tempdb spills remain. It is tempting because redistribution is the standard fix for skewed data and costly shuffle operations in dedicated SQL pools, which are common causes of slow queries.
- ✗
Rebuild all columnstore indexes.
Why it's wrong here
Rebuilding columnstore indexes restores compression and segment quality, improving scan efficiency, but does not change the memory granted to a query, so spills continue. It is tempting because fragmented columnstore indexes do degrade query performance, and rebuilding is a standard maintenance remedy for slow dedicated SQL pool queries.
- ✗
Add partitioning to the tables.
Why it's wrong here
Partitioning splits data for pruning and load management; it does not enlarge the memory grant or reduce per-operator memory demand, so tempdb spills persist. It is tempting because partitioning genuinely helps large scans and data loading in dedicated SQL pools, but spill reduction requires addressing insufficient memory allocation or poor cardinality estimates.
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