DP-203 Design and implement data storage Practice Question
You are designing a data storage solution in Azure Synapse Analytics. You need to store large fact tables that are frequently joined with dimension tables on a common column. The solution must minimize data movement during query execution and support high-concurrency queries. Which two actions should you take? (Choose two.)
⚠ Common exam trap
The trap here is focusing solely on indexing or distribution method without considering the join column alignment, which is key to minimizing 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
✓
Use hash distribution on the join column for the fact table.
Hash distributing the fact table on the join column and replicating the dimension tables minimizes data movement by colocating matching rows and eliminating shuffling of dimension data. These actions directly address the need for efficient joins and high concurrency in Azure Synapse Analytics dedicated SQL pools.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use hash distribution on the join column for the fact table.
Why this is correct
Hash distribution on the join column colocates rows with the same key on the same distribution, minimizing data movement during joins. This is especially effective for large fact tables joined with dimension tables on that key. It improves query performance by reducing shuffling across distributions, which is critical for high-concurrency workloads.
- ✗
Use round-robin distribution for the fact table.
Why it's wrong here
Round-robin distribution spreads data evenly but does not colocate related rows. Joins on a common column require data movement to align rows, increasing query time and resource usage. This contradicts the goal of minimizing data movement. Round-robin is better for staging tables or when no clear join key exists.
- ✓
Use replicated tables for dimension tables.
Why this is correct
Replicating dimension tables copies them to every compute node, eliminating the need to shuffle dimension data during joins. This reduces data movement and improves query performance, particularly for star-schema queries. It is best for smaller dimension tables that are frequently joined, as it avoids the overhead of hash distribution on those tables.
- ✗
Use a columnstore index on the fact table.
Why it's wrong here
Columnstore indexes improve compression and query performance for analytical workloads, but they do not address data movement during joins. The requirement is to minimize data movement and support high concurrency, which is achieved through distribution strategies. Columnstore is beneficial but not a direct solution for the stated goal.
- ✗
Use hash distribution on a column with high cardinality for the fact table.
Why it's wrong here
While high cardinality can help balance data, the distribution column must align with join columns to minimize data movement. If the high-cardinality column is not the join key, joins will still require shuffling. The scenario specifies joins on a common column, so hash distribution should be on that column, not just any high-cardinality column.
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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.