DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are optimizing an Azure Synapse Analytics dedicated SQL pool that contains a large fact table with over 1 billion rows. Queries frequently join this fact table with smaller dimension tables on a distribution key. You notice that many queries perform poorly due to data movement. You need to reduce data movement and improve query performance. Which two actions should you take? (Choose two.)
⚠ Common exam trap
The trap here is focusing on indexing or partitioning, which improve storage and filtering but do not eliminate data movement during joins; distribution design is key.
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
✓
Use hash distribution on the fact table's join column.
Hash distributing the fact table on the join column and replicating the dimension tables co-locate related data, minimizing data movement during joins. This is a standard optimization in Azure Synapse Analytics dedicated SQL pools. Other options do not address the root cause of 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.
- ✗
Partition the fact table by date.
Why it's wrong here
Partitioning can improve query performance by enabling partition elimination for date filters, but it does not reduce data movement during joins. Data movement is about moving rows between distributions, which is governed by distribution keys. Partitioning is orthogonal to distribution and does not solve the join performance issue described.
- ✗
Use round-robin distribution for the fact table.
Why it's wrong here
Round-robin distribution spreads rows evenly across distributions but does not co-locate rows with the same join key. As a result, joins with dimension tables require shuffling data across distributions, increasing data movement and degrading performance. Round-robin is suitable for staging tables or tables without frequent joins, not for large fact tables used in analytical queries.
- ✗
Create a clustered columnstore index on the fact table.
Why it's wrong here
Clustered columnstore indexes improve compression and query performance for analytical workloads, but they do not reduce data movement during joins. Data movement is primarily determined by distribution strategy. While columnstore is beneficial, it does not address the root cause of poor join performance due to distribution misalignment.
- ✓
Use hash distribution on the fact table's join column.
Why this is correct
Hash distribution on the join column ensures that rows with the same join key are stored on the same distribution. When joining with dimension tables that are replicated or similarly distributed, this co-location minimizes data movement across distributions. For large fact tables, choosing the most common join column as the distribution key is a best practice to reduce shuffle operations and improve query performance.
- ✓
Replicate the dimension tables.
Why this is correct
Replicating dimension tables creates a full copy of each dimension on every distribution. This eliminates the need to shuffle dimension data during joins with the fact table, because each distribution has a local copy. For small dimension tables, replication is ideal and significantly reduces data movement. It is a recommended strategy in Azure Synapse Analytics dedicated SQL pools.
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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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