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.
Option C is correct because creating statistics on columns used in WHERE clauses gives the dedicated SQL pool's query optimizer accurate cardinality estimates, enabling better join orders and scan strategies for large fact tables. Option E is correct because partitioning a large fact table on a date column enables partition elimination, so queries filtering on that date range scan only relevant partitions instead of the entire table. Options A and B are incorrect: replicated distribution copies the full fact table to every compute node, which is meant for small dimension tables and would be prohibitively expensive for large fact tables, while round-robin distribution is a generic fallback that does not align data for joins and causes costly data movement. Option D is incorrect because clustered columnstore indexes are the recommended default for large fact tables in dedicated SQL pools, delivering high compression and fast analytical scans, whereas a clustered (rowstore) index is generally inferior for this workload.
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 copies the full table to every compute node, which is designed for small dimension tables; on a large fact table it causes massive storage duplication and slow loads. It would be correct for a small dimension table joined frequently.
- ✗
Use round-robin distribution to evenly distribute data.
Why it's wrong here
Round-robin spreads rows evenly but forces data movement (shuffle) during joins on fact tables, degrading performance. It suits staging or temporary tables where no consistent join key exists, not large fact tables queried with dimension joins.
- ✓
Create statistics on columns used in WHERE clauses.
Why this is correct
Creating statistics on filtered columns gives the dedicated SQL pool's query optimiser accurate cardinality estimates, enabling it to choose better join strategies and avoid full table scans. This directly satisfies the stem's large fact table constraint, where stale or missing statistics cause poor distribution-aware execution plans and inflated data movement.
- ✗
Use clustered index instead of columnstore index.
Why it's wrong here
Clustered indexes suit small tables and point lookups; on large fact tables they cannot match columnstore's compression and batch-mode scan throughput. Replacing columnstore sacrifices the segment elimination and column pruning that dedicated SQL pool relies on for analytical queries.
- ✓
Implement table partitioning on a date column.
Why this is correct
Partitioning on a date column enables partition elimination, so queries filtering by date read only relevant partitions rather than the whole fact table. This reduces I/O and scan volume, directly improving query performance on large dedicated SQL pool fact tables.
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