DP-203 Design and implement data storage Practice Question
A financial services company is migrating its data warehouse to Azure Synapse Analytics. They have a star schema with a 10-billion-row fact table and 50 dimension tables. Query performance is critical, and they need to minimize data movement during joins. Which distribution strategy should they use for the fact table?
⚠ Common exam trap
Watch out — candidates often confuse table partitioning with distribution strategy, assuming 'partitioned distribution' is a valid option, but Azure Synapse Analytics uses hash, round-robin, and replicated distributions only, while partitioning is a separate concept for data organization within a distribution.
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
✓
Hash distribution on the most frequently joined dimension key
Hash distribution on the most frequently joined dimension key is correct because it co-locates matching rows from the fact and dimension tables on the same compute node, minimizing data movement during joins. For a 10-billion-row fact table, this distribution ensures that the most common join operation is performed locally without shuffling data across nodes, which is critical for query performance in Azure Synapse Analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replicated distribution
Why it's wrong here
Not suitable for large fact tables.
- ✗
Partitioned distribution
Why it's wrong here
Not a valid distribution strategy.
- ✓
Hash distribution on the most frequently joined dimension key
Why this is correct
Collocates data for joins.
- ✗
Round-robin distribution
Why it's wrong here
Causes data movement during joins.
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