DP-203 Design and implement data storage Practice Question
Which THREE are best practices for optimizing query performance in Azure Synapse Analytics dedicated SQL pool?
⚠ Common exam trap
A common mix-up: candidates confuse resource class with performance optimization, assuming larger resource classes always speed up queries, when in fact they reduce concurrency and can cause resource contention, making them a poor general-purpose best practice.
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
✓
Use materialized views for complex aggregations
Materialized views precompute and store the results of complex aggregations, such as SUM, COUNT, AVG, or GROUP BY operations, in Azure Synapse dedicated SQL pool. When a query references the same aggregation pattern, the optimizer can automatically substitute the materialized view, significantly reducing compute and I/O overhead by avoiding full table scans and recomputation. This is a best practice for improving performance on repetitive analytical workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use materialized views for complex aggregations
Why this is correct
Pre-computes results.
- ✗
Use the largest resource class for all queries
Why it's wrong here
Reduces concurrency.
- ✓
Create clustered columnstore indexes
Why this is correct
Improves compression and query performance.
- ✓
Use hash distribution on columns used in JOINs
Why this is correct
Minimizes data shuffling.
- ✗
Use round-robin distribution for large fact tables
Why it's wrong here
Not optimal; leads to data movement.
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