- A
Add a non-clustered index on frequently filtered columns.
Why wrong: Columnstore tables are optimized for scans; adding non-clustered indexes can help point lookups but not scan-heavy queries.
- B
Change the distribution column to a column with higher cardinality.
Why wrong: The date key already has high cardinality; changing distribution key would not solve fragmentation and would require significant rework.
- C
Change the distribution to round-robin.
Why wrong: Round-robin distribution distributes rows evenly but increases data movement during joins, potentially worsening performance.
- D
Rebuild the clustered columnstore index.
Rebuilding the columnstore index improves compression, removes deleted rows, and reorganizes rowgroups, enhancing scan performance.
Quick Answer
The correct action is to rebuild the clustered columnstore index. Over time, frequent insert, update, and delete operations cause fragmentation in columnstore indexes, creating suboptimal row groups with deleted records and reduced compression. Rebuilding the index forces a full reorganization of data into maximally compressed row groups, restoring segment elimination and high compression that directly improves query performance in Azure Synapse Analytics dedicated SQL pools. On the DP-203 exam, this scenario tests your understanding of columnstore index maintenance and the difference between rebuilding and reorganizing—a common trap is choosing ALTER INDEX REORGANIZE, which only defragments without fully removing deleted rows. Remember the tip: “Rebuild to remove ghosts, reorganize to tidy the shelves.”
DP-203 Practice Question: Monitor and optimize data storage and processing
This DP-203 practice question tests your understanding of monitor and optimize data storage and processing. 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. They notice that queries against a large fact table are running slower over time. The table is hash-distributed on a date key and has a clustered columnstore index. Which action should you take to 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
Rebuild the clustered columnstore index.
Over time, columnstore indexes can become fragmented due to insert, update, and delete operations, leading to compressed row groups that are not optimally sized or have deleted records. Rebuilding the clustered columnstore index reorganizes the data into fully compressed row groups, removes deleted rows, and restores the high compression and segment elimination that columnstore indexes rely on for fast query 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.
- ✗
Add a non-clustered index on frequently filtered columns.
Why it's wrong here
Columnstore tables are optimized for scans; adding non-clustered indexes can help point lookups but not scan-heavy queries.
- ✗
Change the distribution column to a column with higher cardinality.
Why it's wrong here
The date key already has high cardinality; changing distribution key would not solve fragmentation and would require significant rework.
- ✗
Change the distribution to round-robin.
Why it's wrong here
Round-robin distribution distributes rows evenly but increases data movement during joins, potentially worsening performance.
- ✓
Rebuild the clustered columnstore index.
Why this is correct
Rebuilding the columnstore index improves compression, removes deleted rows, and reorganizes rowgroups, enhancing scan performance.
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 may assume performance degradation is always due to data skew or distribution choice, overlooking the common real-world issue of columnstore index fragmentation from ongoing DML operations.
Detailed technical explanation
How to think about this question
Columnstore indexes store data in compressed column segments called row groups, each ideally containing 1,048,576 rows. Fragmentation occurs when row groups are not fully compressed (e.g., due to small inserts) or contain deleted rows marked as 'deleted' in a delta store, causing queries to scan more data than necessary. Rebuilding the index compresses all row groups to the optimal size and eliminates deleted rows, restoring the full benefit of batch mode execution and predicate pushdown.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Rebuild the clustered columnstore index. — Over time, columnstore indexes can become fragmented due to insert, update, and delete operations, leading to compressed row groups that are not optimally sized or have deleted records. Rebuilding the clustered columnstore index reorganizes the data into fully compressed row groups, removes deleted rows, and restores the high compression and segment elimination that columnstore indexes rely on for fast query 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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Last reviewed: Jun 11, 2026
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.
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