The answer is to replicate the Customer dimension table to all compute nodes. This is correct because the fact table is already hash-distributed on CustomerID, which co-locates matching rows from the fact and dimension tables on the same node when the dimension is replicated, eliminating costly data movement during joins. Changing the fact table’s distribution would break this co-location and increase shuffle overhead, while replicating the smaller 10 GB dimension table ensures every node has a local copy, drastically improving join performance. On the DP-203 exam, this scenario tests your understanding of Synapse’s distribution strategies and the trade-off between hash distribution for large fact tables and replication for smaller dimension tables—a common trap is assuming you must change the fact table’s distribution instead. Remember the memory tip: “Replicate the small, hash the large, and joins will not barge.”
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Your company uses Azure Synapse Analytics for a data warehouse. The fact table is 500 GB and distributed by hash on CustomerID. You notice that queries joining the fact table with the Customer dimension table are slow due to data movement. The Customer dimension table is 10 GB. Which THREE actions should you take to improve query performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the fact table distribution to hash on CustomerID
Option C is correct because the fact table is already distributed by hash on CustomerID, which is the join key with the Customer dimension table. This co-locates matching rows on the same compute node, eliminating data movement during joins. Keeping this distribution is essential for performance; changing it would break co-location and increase shuffle overhead.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 heap index on the fact table
Why it's wrong here
Heap index lacks compression and columnar performance.
✗
Change the fact table distribution to round-robin
Why it's wrong here
Round-robin would increase data movement.
✓
Change the fact table distribution to hash on CustomerID
Why this is correct
Hash distribution on the join key colocates data with the dimension.
Related concept
Read the scenario before looking for a memorised answer.
✓
Implement partitioning on the fact table by date
Why this is correct
Partition elimination can reduce data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✓
Replicate the Customer dimension table to all compute nodes
Why this is correct
Replication avoids data movement during joins.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might think changing the distribution method (e.g., to round-robin) would help, but they overlook that the fact table is already correctly hash-distributed on the join key, and the real issue is the dimension table not being replicated, causing unnecessary data movement.
Detailed technical explanation
How to think about this question
In Azure Synapse, hash distribution uses a deterministic hash function on the distribution column to assign rows to distributions (60 distributions per node). Co-located joins occur when both tables are hash-distributed on the same join key, allowing each distribution to process its local data without shuffling. Replicating the Customer dimension table (10 GB) to all compute nodes is feasible because Synapse supports replicated tables up to 60 GB, which avoids data movement entirely for star schema joins.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the fact table distribution to hash on CustomerID — Option C is correct because the fact table is already distributed by hash on CustomerID, which is the join key with the Customer dimension table. This co-locates matching rows on the same compute node, eliminating data movement during joins. Keeping this distribution is essential for performance; changing it would break co-location and increase shuffle overhead.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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