Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A company uses Azure SQL Database for an e-commerce system. The Orders table has millions of rows with a clustered index on OrderID (the primary key). Queries that filter on OrderDate and CustomerID to find recent orders for a specific customer are very slow. Which indexing strategy will most improve the performance of these queries?
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
Distractor review
Create a nonclustered index on OrderID only
OrderID is already the clustered index, so another index on OrderID would be redundant and would not help because the query filters on OrderDate and CustomerID, not OrderID.
B
Distractor review
Create separate nonclustered indexes on OrderDate and CustomerID
Separate indexes allow the database to use either one, but not both simultaneously in an efficient way. The optimizer may use one index and then perform lookups, which is less efficient than a composite index that covers both columns.
C
Best answer
Create a nonclustered composite index on (OrderDate, CustomerID)
A composite index on both columns allows the database to find rows matching both filter conditions in a single index seek. This is the most efficient strategy for queries that filter on multiple columns together.
D
Distractor review
Create a clustered index on CustomerID instead of OrderID
Changing the clustered index would reorganize the entire table and might improve some queries, but OrderID is likely used for joins and lookups. Moreover, a clustered index on CustomerID would not directly benefit the filter on OrderDate. A nonclustered composite index is less disruptive and more targeted.
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 nonclustered composite index on (OrderDate, CustomerID) — Creating a nonclustered composite index on (OrderDate, CustomerID) will allow the database engine to quickly locate rows that match both filter conditions without scanning the entire table. The order of columns should match the query predicate; since OrderDate is often used with range conditions and CustomerID for equality, placing the more selective column first can vary, but a composite index covering both columns is the most effective approach. Including additional columns (like TotalAmount) could further improve performance as a covering index.
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.
Discussion
Loading comments…
Sign in to join the discussion.