Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
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
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
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
Distractor review
Create a nonclustered index on the ProductID column.
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
Distractor review
Create a filtered index for the most recent month's data.
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
Distractor review
Create a clustered rowstore index (default) and rely on database compression.
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 trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
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. — For analytical queries that scan large amounts of data and perform aggregations, a columnstore index is highly effective. Columnstore indexes store data in a columnar format, which allows for better compression and faster scanning of only the columns needed for aggregation queries. This can lead to dramatic performance improvements (often 10x or more) for such workloads. A clustered rowstore index (the default) is optimized for point lookups and transactional workloads. Nonclustered indexes can help with filtering, but they are not as efficient as columnstore for large scans and aggregations. A filtered index is used for a subset of rows and does not address the full scan requirement.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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