Question 630 of 760
DP-203 Develop data processing Practice Question
You are using Azure Synapse Analytics dedicated SQL pool to run a query that joins a large fact table (10 billion rows) and a small dimension table (1 million rows). The query is slow. Which distribution strategy should you use for the dimension table to improve performance?
⚠ Common exam trap
Many candidates choose hash distribution on the foreign key (Option D) thinking it aligns the join keys, but they overlook that the fact table is typically distributed on a different column (e.g., its own primary key or a date column), so the join still requires data movement, whereas replication is the optimal strategy for small dimension tables in a star schema.
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
✓
Replicate the dimension table to all compute nodes.
Replicating the small dimension table (1 million rows) to all compute nodes eliminates data movement during the join with the large fact table (10 billion rows). In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so the join can be performed locally on every node without shuffling data across the network, drastically reducing query latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Round-robin distribute the dimension table.
Why it's wrong here
Round-robin still requires data movement for join.
- ✗
Hash-distribute the dimension table on its primary key.
Why it's wrong here
Hash distribution would require shuffling during join with fact table.
- ✓
Replicate the dimension table to all compute nodes.
Why this is correct
Replication avoids data movement for small tables.
- ✗
Hash-distribute the dimension table on the foreign key column.
Why it's wrong here
Still requires shuffle; replication is better.
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Last reviewed: Jun 24, 2026
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