DP-203 Develop data processing Practice Question
You are optimizing an Azure Synapse Analytics dedicated SQL pool that processes large fact tables. You need to improve query performance for a common join between a fact table and a dimension table. The fact table is distributed using hash distribution on a column that is not the join key. The dimension table is small and replicated. You want to minimize data movement during the join. What should you do?
⚠ Common exam trap
The trap here is assuming that any performance optimization like indexing or materialized views will reduce data movement, when distribution alignment is the key factor.
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
✓
Change the distribution of the fact table to hash distribute on the join key.
To minimize data movement during a join in a dedicated SQL pool, the distribution key of the large fact table should match the join key. This co-locates matching rows on the same distribution, avoiding shuffling. Round-robin distribution scatters data, materialized views do not change distribution, and columnstore indexes improve storage and scan efficiency but not 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.
- ✗
Add a clustered columnstore index on the join key column.
Why it's wrong here
A clustered columnstore index is the default and provides compression and query performance benefits, but it does not affect data distribution across distributions. The join still requires data movement if the distribution keys do not align. Therefore, it does not minimize data movement for the join.
- ✓
Change the distribution of the fact table to hash distribute on the join key.
Why this is correct
Hash distributing the fact table on the join key aligns the data so that rows with the same join key values are co-located. This eliminates the need for data movement during the join with the dimension table, which is already replicated. This is the most effective way to minimize data movement and improve query performance.
- ✗
Change the distribution of the fact table to round-robin.
Why it's wrong here
Round-robin distribution spreads data evenly but does not co-locate rows with matching join keys. During a join, data must be shuffled across distributions, increasing data movement and degrading performance. This would not minimize data movement and is not appropriate for large fact tables frequently joined on a specific key.
- ✗
Create a materialized view that pre-joins the fact and dimension tables.
Why it's wrong here
A materialized view can improve performance for specific queries, but it does not address the underlying data movement during joins. It also requires maintenance and storage. While it might help, it is not the direct solution to minimize data movement for the join itself, especially if the join key distribution is misaligned.
Go deeper
Related to this question
About these practice questions
This DP-203 question is part of Courseiva's 509-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.