The correct choice is a clustered columnstore index. This index type is specifically designed to improve aggregation performance for analytical queries in Azure SQL Database because it stores data in a columnar format rather than row-by-row, allowing the database engine to read only the columns needed for SUM, COUNT, and AVG operations. This drastically reduces I/O and leverages batch processing and advanced compression, making it ideal for large fact tables with millions of rows in monthly reporting workloads. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of workload optimization—know that clustered columnstore indexes are for heavy analytical queries, while traditional rowstore indexes (like clustered or nonclustered) are better for transactional lookups. A common trap is confusing columnstore with column-level security or thinking any index will suffice; remember that columnstore excels at aggregating many rows, not finding a single row. Memory tip: “Columnstore for columns of data you count and sum; rowstore for rows you find one by one.”
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 uses Azure SQL Database to store a large fact table of sales transactions with millions of rows. They run complex aggregate queries (SUM, COUNT, AVG) across many rows for monthly reports. These queries take too long. Which index type should they add to the table to improve performance?
Why wrong: Incorrect. B-tree indexes are optimized for point lookups and range scans, not large aggregations.
B
Nonclustered rowstore index
Why wrong: Incorrect. Nonclustered rowstore indexes improve find operations but are not designed for large-scale aggregations.
C
Clustered columnstore index
Correct. A clustered columnstore index is ideal for data warehousing and analytical workloads, significantly improving aggregate query performance.
D
Nonclustered columnstore index
Why wrong: Incorrect. While a nonclustered columnstore index can also help, a clustered columnstore index is the primary storage structure for large fact tables and provides better performance for full-table scans.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Clustered columnstore index
Clustered columnstore indexes are optimized for large fact tables and analytical workloads because they store data in a columnar format, which significantly reduces the amount of data read from disk for aggregate queries like SUM, COUNT, and AVG. This index type also uses batch processing and compression to accelerate query performance on millions of rows, making it ideal for monthly reporting 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.
✗
Clustered B-tree index
Why it's wrong here
Incorrect. B-tree indexes are optimized for point lookups and range scans, not large aggregations.
✗
Nonclustered rowstore index
Why it's wrong here
Incorrect. Nonclustered rowstore indexes improve find operations but are not designed for large-scale aggregations.
✓
Clustered columnstore index
Why this is correct
Correct. A clustered columnstore index is ideal for data warehousing and analytical workloads, significantly improving aggregate query performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Nonclustered columnstore index
Why it's wrong here
Incorrect. While a nonclustered columnstore index can also help, a clustered columnstore index is the primary storage structure for large fact tables and provides better performance for full-table scans.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse nonclustered columnstore indexes with clustered columnstore indexes, assuming any columnstore index will suffice, but only the clustered version is designed for large fact tables with heavy aggregation workloads and avoids the overhead of maintaining a separate rowstore index.
Detailed technical explanation
How to think about this question
Columnstore indexes use a columnar data format where each column is stored separately, enabling vectorized execution and high compression ratios (often 10x or more) that reduce I/O. Under the hood, Azure SQL Database uses batch mode processing for columnstore indexes, which processes rows in batches of up to 900 rows at a time, dramatically improving CPU efficiency for aggregate queries. In real-world scenarios, switching from a rowstore to a clustered columnstore index can reduce query times for monthly sales reports from minutes to seconds.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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: Clustered columnstore index — Clustered columnstore indexes are optimized for large fact tables and analytical workloads because they store data in a columnar format, which significantly reduces the amount of data read from disk for aggregate queries like SUM, COUNT, and AVG. This index type also uses batch processing and compression to accelerate query performance on millions of rows, making it ideal for monthly reporting 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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