Question 275 of 851

Design and implement data storage →hardMultiple SelectObjective-mapped

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?

A
Use heap index on the fact table
Why wrong: Heap indexes are not optimal for large fact tables in Azure Synapse Analytics; clustered columnstore indexes are recommended for columnstore compression and better query performance.
B
Change the fact table distribution to round-robin
Why wrong: Changing to round-robin distribution would distribute data randomly, increasing data movement during joins with the dimension table, thus degrading performance.
C
Change the fact table distribution to hash on CustomerID
Why wrong: The fact table is already hash-distributed on CustomerID. 'Changing' to the same distribution is not a valid action and does not improve performance.
D
Implement partitioning on the fact table by date
Implementing partitioning by date allows partition elimination for queries that filter on date columns, reducing the amount of data scanned and improving performance.
E
Replicate the Customer dimension table to all compute nodes
Replicating the Customer dimension table ensures every compute node has a full copy, eliminating data movement during joins with the fact table.

Full breakdown with real-world context →

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

✓

Implement partitioning on the fact table by date

The fact table is already hash-distributed on CustomerID, which is the correct distribution key for joining with the Customer dimension. Changing it to the same distribution is not an actionable improvement. To reduce data movement, replicate the Customer dimension table (E) so each compute node has a local copy, and implement partitioning on the fact table by date (D) to enable partition elimination for queries filtered by date. Options A and B are incorrect: heap indexes are not suitable for large fact tables, and round-robin distribution would worsen join performance.

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 indexes are not optimal for large fact tables in Azure Synapse Analytics; clustered columnstore indexes are recommended for columnstore compression and better query performance.
✗
Change the fact table distribution to round-robin
Why it's wrong here
Changing to round-robin distribution would distribute data randomly, increasing data movement during joins with the dimension table, thus degrading performance.
✗
Change the fact table distribution to hash on CustomerID
Why it's wrong here
The fact table is already hash-distributed on CustomerID. 'Changing' to the same distribution is not a valid action and does not improve performance.
✓
Implement partitioning on the fact table by date
Why this is correct
Implementing partitioning by date allows partition elimination for queries that filter on date columns, reducing the amount of data scanned and improving performance.
Related concept
Read the scenario before looking for a memorised answer.
✓
Replicate the Customer dimension table to all compute nodes
Why this is correct
Replicating the Customer dimension table ensures every compute node has a full copy, eliminating data movement during joins with the fact table.
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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-203 question test?

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: Implement partitioning on the fact table by date — The fact table is already hash-distributed on CustomerID, which is the correct distribution key for joining with the Customer dimension. Changing it to the same distribution is not an actionable improvement. To reduce data movement, replicate the Customer dimension table (E) so each compute node has a local copy, and implement partitioning on the fact table by date (D) to enable partition elimination for queries filtered by date. Options A and B are incorrect: heap indexes are not suitable for large fact tables, and round-robin distribution would worsen join performance.

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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