DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your team is troubleshooting slow query performance on a dedicated SQL pool in Azure Synapse Analytics. The query uses a hash-distributed fact table with 60 distributions. After reviewing the execution plan, you notice a high number of data moves. Which action would most likely reduce data movement?
⚠ Common exam trap
The trap is thinking that updating statistics or changing distribution type (e.g., to round-robin) will reduce data movement, when the key is aligning distribution with join columns.
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
✓
Redistribute the fact table on the join column using hash distribution.
Redistributing the fact table on the join column using hash distribution will most likely reduce data movement. In a dedicated SQL pool, hash distribution on the join column ensures that rows with the same join key are colocated on the same distribution, minimizing the need to shuffle data during joins.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the distribution type to round-robin.
Why it's wrong here
Round-robin spreads rows evenly but destroys co-location, so every join must shuffle all 60 distributions, increasing data movement. Round-robin suits staging or loading tables with no join keys, not fact tables repeatedly joined on a shared column.
- ✗
Update statistics on all columns used in joins.
Why it's wrong here
Statistics inform cardinality estimates, not distribution placement; stale stats cause poor plans but cannot eliminate data movement when joined columns are not the distribution key. Updating statistics is the right fix for skewed estimates producing bad join strategies, not for a hash-distributed table whose joins require shuffling.
- ✗
Increase the number of distributions to 120.
Why it's wrong here
Raising distributions to 120 splits each distribution's rows further, multiplying shuffle operations across more nodes without co-locating join keys. More distributions help when a single distribution exceeds compute capacity, not when joins move data because keys differ from the distribution column.
- ✓
Redistribute the fact table on the join column using hash distribution.
Why this is correct
Hash-distributing the fact table on the join column co-locates matching rows in the same distribution, so joins execute locally instead of shuffling data across the 60 distributions. This directly reduces the high data movement identified in the execution plan.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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