DP-203 Design and implement data storage Practice Question
Which TWO actions should you take to optimize query performance in Azure Synapse Analytics dedicated SQL pool when working with large fact tables?
⚠ Common exam trap
It's easy for candidates to confuse distribution methods (replicated, round-robin, hash) with performance tuning for large fact tables, overlooking that statistics maintenance is a critical and separate optimization step that directly impacts query plan quality.
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
✓
Create statistics on columns used in WHERE clauses.
Creating statistics on columns used in WHERE clauses enables the Azure Synapse Analytics dedicated SQL pool query optimizer to generate more accurate cardinality estimates, leading to better join strategies and index selections. Without up-to-date statistics, the optimizer may choose suboptimal plans, especially for large fact tables where data distribution skew is common.
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 replicated distribution for the fact table.
Why it's wrong here
Replicated distribution is only for small dimension tables.
- ✗
Use round-robin distribution to evenly distribute data.
Why it's wrong here
Round-robin causes data movement for joins and aggregations.
- ✓
Create statistics on columns used in WHERE clauses.
Why this is correct
Statistics help the optimizer choose efficient query plans.
- ✗
Use clustered index instead of columnstore index.
Why it's wrong here
Columnstore indexes are optimized for large fact tables; clustered indexes are not.
- ✓
Implement table partitioning on a date column.
Why this is correct
Partition elimination reduces the amount of data scanned.
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