DP-203 Design and implement data storage Practice Question
A company uses Azure Synapse Analytics dedicated SQL pool to store sales data. The fact table is partitioned by date and distributed by product ID. Queries often join the fact table with a small dimension table on product ID. You notice that these joins cause significant data movement. You need to minimize data movement for these joins. What should you do?
⚠ Common exam trap
Many candidates confuse partitioning with distribution; partitioning does not co-locate rows across compute nodes, so it cannot eliminate join data movement.
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 and distribute the fact table by product ID.
Replicating a small dimension table and hash-distributing the large fact table on the join key eliminates data movement because each compute node has a local copy of the dimension and only its subset of fact rows. This is a standard technique in Azure Synapse Analytics dedicated SQL pool to optimize join performance. Other options either increase data movement or do not address the distribution mismatch.
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 of the fact table to round-robin.
Why it's wrong here
Round-robin distribution spreads rows evenly but does not co-locate related data. Joins on product ID would require shuffling both tables to align rows, increasing data movement. Round-robin is suitable for staging tables or when no clear join key exists, but it is not optimal for frequent joins on a specific column. It would worsen the data movement issue.
- ✓
Replicate the dimension table and distribute the fact table by product ID.
Why this is correct
Replicating the dimension table creates a full copy on every compute node. When the fact table is distributed by product ID, each node can join its local fact rows with the replicated dimension without shuffling data. This eliminates data movement for the join. Replication is ideal for small dimension tables (typically under 2 GB compressed) and is a recommended pattern in dedicated SQL pool.
- ✗
Partition the dimension table by product ID and use hash distribution on the fact table.
Why it's wrong here
Partitioning the dimension table by product ID does not reduce data movement because partitioning is a physical division within a distribution, not across distributions. The join still requires aligning rows by product ID across distributions. Hash distribution on the fact table is already in place; the dimension table's distribution is the key factor. This option misuses partitioning.
- ✗
Create a materialized view that pre-joins the fact and dimension tables.
Why it's wrong here
A materialized view can improve performance for repetitive queries, but it does not address the underlying data movement during joins. The view itself would still require joins if not fully pre-aggregated, and maintenance overhead may be high. In dedicated SQL pool, materialized views are better for aggregations, not for eliminating join data movement. The core issue is distribution design.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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