- A
Create a clustered columnstore index on the table.
A clustered columnstore index is designed for analytical workloads. It compresses data and allows efficient scanning of columns, making aggregation queries much faster.
- B
Create a nonclustered index on the ProductID column.
Why wrong: A nonclustered index would speed up queries that filter on ProductID, but the queries are scanning millions of rows and aggregating. Columnstore is more efficient for such scans.
- C
Create a filtered index for the most recent month's data.
Why wrong: A filtered index covers only a subset of rows. If queries frequently scan historical data as well, a filtered index will not help. Also, it is still a rowstore index.
- D
Create a clustered rowstore index (default) and rely on database compression.
Why wrong: A clustered rowstore index is optimized for OLTP workloads. Compression helps but does not provide the same performance gains as a columnstore index for aggregation queries.
DP-900 Practice Question: Identify considerations for relational data on Azure
This DP-900 practice question tests your understanding of identify considerations for relational data 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 retail company runs analytical reporting queries on a large Sales table in Azure SQL Database. The table contains over 100 million rows and is updated daily with new transactions. The queries aggregate data by product and month, scanning millions of rows per query. The company wants to significantly reduce query execution time without changing the queries. Which indexing strategy should they implement?
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
Create a clustered columnstore index on the table.
A clustered columnstore index is ideal for large data warehousing and analytical workloads because it stores data column-wise, enabling high compression and batch-mode processing. For queries that aggregate millions of rows by product and month, columnstore indexes dramatically reduce I/O and CPU by scanning only the necessary columns and using segment elimination, which directly addresses the requirement to reduce query execution time without changing the 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.
- ✓
Create a clustered columnstore index on the table.
Why this is correct
A clustered columnstore index is designed for analytical workloads. It compresses data and allows efficient scanning of columns, making aggregation queries much faster.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a nonclustered index on the ProductID column.
Why it's wrong here
A nonclustered index would speed up queries that filter on ProductID, but the queries are scanning millions of rows and aggregating. Columnstore is more efficient for such scans.
- ✗
Create a filtered index for the most recent month's data.
Why it's wrong here
A filtered index covers only a subset of rows. If queries frequently scan historical data as well, a filtered index will not help. Also, it is still a rowstore index.
- ✗
Create a clustered rowstore index (default) and rely on database compression.
Why it's wrong here
A clustered rowstore index is optimized for OLTP workloads. Compression helps but does not provide the same performance gains as a columnstore index for aggregation queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a nonclustered index (B) thinking it will speed up all queries, but they overlook that analytical aggregations on millions of rows require columnstore's batch processing and column elimination, not row-based index seeks.
Detailed technical explanation
How to think about this question
Under the hood, a clustered columnstore index organizes data into column segments of roughly 1 million rows each, and SQL Server uses batch-mode execution to process these segments in parallel, often achieving 10x or more performance gains over rowstore for aggregation queries. A subtle behavior is that columnstore indexes are updateable in SQL Database, but frequent small inserts or updates can cause delta stores to grow, requiring periodic reorganization to maintain optimal compression and performance. In real-world scenarios, companies often combine columnstore with partitioning to enable partition elimination for time-based queries, further reducing I/O.
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.
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FAQ
Questions learners often ask
What does this DP-900 question test?
Identify considerations for relational data on Azure — This question tests Identify considerations for relational data on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a clustered columnstore index on the table. — A clustered columnstore index is ideal for large data warehousing and analytical workloads because it stores data column-wise, enabling high compression and batch-mode processing. For queries that aggregate millions of rows by product and month, columnstore indexes dramatically reduce I/O and CPU by scanning only the necessary columns and using segment elimination, which directly addresses the requirement to reduce query execution time without changing the 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.
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Last reviewed: Jun 11, 2026
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