- A
Replicate the Store dimension table
Correct. Replicating a small dimension table (less than 1 GB) copies it to all distributions, eliminating data movement when joining with the fact table. This directly addresses the performance issue.
- B
Change the distribution of the fact table to round-robin
Why wrong: Incorrect. Round-robin distribution distributes data evenly but does not colocate data for joins, likely increasing data movement and slowing queries further.
- C
Change the distribution key of the fact table to StoreID
Why wrong: Incorrect. This would require rebuilding the entire table and may not help because the fact table joins with both Store and Product dimensions. Data movement may still occur for the Product join.
- D
Add a nonclustered index on the StoreID column in the fact table
Why wrong: Incorrect. Indexes in Synapse dedicated SQL pool are used differently (columnstore is default). A nonclustered index on a hash-distributed table does not reduce data movement, which is the primary cause of slow joins.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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.
A company uses Azure Synapse Analytics dedicated SQL pool for a large data warehouse. The fact table contains billions of rows and is hash-distributed on ProductID. Frequent queries join this fact table with a small Store dimension table (10,000 rows) and a medium-sized Product dimension table (500,000 rows). The queries aggregate sales by store and product for recent months, but run slowly due to data movement during joins. Which design change will most reduce data movement and improve query performance?
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 Store dimension table
Replicating the small Store dimension table (10,000 rows) across all compute nodes eliminates the need to shuffle data during joins with the fact table. In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so queries that join a replicated table with a distributed fact table avoid costly data movement, significantly improving performance for frequent aggregation queries.
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.
- ✓
Replicate the Store dimension table
Why this is correct
Correct. Replicating a small dimension table (less than 1 GB) copies it to all distributions, eliminating data movement when joining with the fact table. This directly addresses the performance issue.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the distribution of the fact table to round-robin
Why it's wrong here
Incorrect. Round-robin distribution distributes data evenly but does not colocate data for joins, likely increasing data movement and slowing queries further.
- ✗
Change the distribution key of the fact table to StoreID
Why it's wrong here
Incorrect. This would require rebuilding the entire table and may not help because the fact table joins with both Store and Product dimensions. Data movement may still occur for the Product join.
- ✗
Add a nonclustered index on the StoreID column in the fact table
Why it's wrong here
Incorrect. Indexes in Synapse dedicated SQL pool are used differently (columnstore is default). A nonclustered index on a hash-distributed table does not reduce data movement, which is the primary cause of slow joins.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think changing the distribution key or adding an index will solve data movement, but they overlook that replicating the small dimension table is the most direct and cost-effective way to eliminate shuffling for frequent joins.
Detailed technical explanation
How to think about this question
Replicated tables in Azure Synapse dedicated SQL pool are stored in full on each of the 60 distributions, so joins with distributed tables require no data movement. The Store dimension at 10,000 rows is well under the recommended 2 GB per table threshold for replication, making it an ideal candidate. This approach is especially effective for star-schema designs where small dimension tables are frequently joined with large fact tables, as it eliminates the broadcast or shuffle operations that otherwise occur.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Describe an analytics workload on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe an analytics workload on Azure practice questions
Targeted practice on this topic area only
- →
All DP-900 questions
982 questions across all exam domains
- →
Microsoft Azure Data Fundamentals DP-900 study guide
Full concept coverage aligned to exam objectives
- →
DP-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe core data concepts practice questions
Practise DP-900 questions linked to Describe core data concepts.
Describe an analytics workload on Azure practice questions
Practise DP-900 questions linked to Describe an analytics workload on Azure.
Identify considerations for relational data on Azure practice questions
Practise DP-900 questions linked to Identify considerations for relational data on Azure.
Describe considerations for working with non-relational data on Azure practice questions
Practise DP-900 questions linked to Describe considerations for working with non-relational data on Azure.
DP-900 fundamentals practice questions
Practise DP-900 questions linked to DP-900 fundamentals.
DP-900 scenario practice questions
Practise DP-900 questions linked to DP-900 scenario.
DP-900 troubleshooting practice questions
Practise DP-900 questions linked to DP-900 troubleshooting.
Practice this exam
Start a free DP-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Replicate the Store dimension table — Replicating the small Store dimension table (10,000 rows) across all compute nodes eliminates the need to shuffle data during joins with the fact table. In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so queries that join a replicated table with a distributed fact table avoid costly data movement, significantly improving performance for frequent aggregation queries.
What should I do if I get this DP-900 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 2026
This DP-900 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-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.