DP-203 Develop data processing Practice Question
You are using Azure Synapse Analytics dedicated SQL pool to process large fact tables. You need to improve query performance for joins between a large fact table and a small dimension table. The dimension table is less than 2 GB. What should you do?
⚠ Common exam trap
The trap here is focusing on indexing or distribution methods that do not eliminate data movement for joins with small tables.
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 distributions.
Replicating a small dimension table in a dedicated SQL pool ensures each distribution has a local copy, eliminating data movement during joins with large fact tables. Hash and round-robin distributions do not guarantee colocation, and columnstore indexes do not solve data movement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Replicate the dimension table to all distributions.
Why this is correct
In a dedicated SQL pool, replicated tables are copied to every distribution. For small dimension tables under 2 GB, replication eliminates data movement during joins with large fact tables, improving performance. This is the recommended strategy for star schema joins.
- ✗
Round-robin distribute the dimension table.
Why it's wrong here
Round-robin distribution spreads rows evenly but does not align with the join key. Joins with the fact table would require data movement, negating performance benefits. Round-robin is suitable for staging tables, not for dimension tables used in joins.
- ✗
Create a clustered columnstore index on the dimension table.
Why it's wrong here
Clustered columnstore indexes improve compression and query performance for large tables, but they do not address data movement during joins. For a small dimension table, replication is the primary optimization. Columnstore alone would not eliminate the shuffle.
- ✗
Hash distribute the dimension table on the join key.
Why it's wrong here
Hash distributing a small dimension table on the join key would colocate rows with the fact table, but the fact table is likely distributed on a different key. This could still require data movement. Replication is more efficient for small tables because it avoids shuffling the larger fact table.
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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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