- A
A. Create a clustered index on (CustomerID, OrderDate) and a non-clustered index on OrderID.
Why wrong: Incorrect. A clustered index on (CustomerID, OrderDate) would make single order lookups by OrderID slower because the clustered index determines the physical order, and OrderID lookups would require a key lookup or a non-clustered index scan.
- B
B. Create a clustered index on OrderID and a non-clustered index on (CustomerID, OrderDate).
Correct. The clustered index on OrderID optimizes single row lookups. The non-clustered index on (CustomerID, OrderDate) supports range queries filtering on those columns. This combination provides optimal performance for both query patterns.
- C
C. Create a non-clustered index on each column used in WHERE clauses: OrderID, CustomerID, and OrderDate.
Why wrong: Incorrect. While individual indexes can be used, they are less efficient for composite range queries. A single composite index on (CustomerID, OrderDate) would better support the range condition and avoid index intersections.
- D
D. Create a clustered columnstore index on the entire table and no other indexes.
Why wrong: Incorrect. Columnstore indexes are designed for analytical workloads and large scans, not for frequent singleton lookups or point queries. They would degrade performance for the transactional queries described.
Quick Answer
The answer is B: a clustered index on OrderID and a non-clustered index on (CustomerID, OrderDate). This is correct because the clustered index physically orders the table by OrderID, making single-row point lookups extremely fast via a single seek, while the non-clustered index on (CustomerID, OrderDate) acts as a covering index for the range query—it allows the database to filter by CustomerID, scan the date range, and return results already sorted by OrderDate without touching the base table. On the DP-900 exam, this question tests your understanding of how index design balances mixed workloads: a common trap is choosing a clustered index on CustomerID, which would slow down point lookups by OrderID. Remember the memory tip: “Point lookup needs a key, range query needs a cover”—the clustered index handles the point, and the composite non-clustered index covers the range and sort.
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. A key principle to apply: a clustered index determines the physical storage order of data rows in a table.. 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 SQL Database for an order management system. The Orders table has columns: OrderID (int, primary key), CustomerID (int), OrderDate (datetime), Status (varchar), and TotalAmount (decimal). The most frequent queries retrieve orders for a specific CustomerID within a date range and order the results by OrderDate. Additionally, single order lookups by OrderID must remain fast. Which indexing strategy best satisfies both requirements?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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
B. Create a clustered index on OrderID and a non-clustered index on (CustomerID, OrderDate).
Option B is correct because a clustered index on OrderID ensures fast single-row lookups by the primary key, while a non-clustered index on (CustomerID, OrderDate) efficiently covers the range query filtering by CustomerID and ordering by OrderDate. This design leverages the clustered index for point lookups and the non-clustered index as a covering index for the most frequent query pattern.
Key principle: A clustered index determines the physical storage order of data rows in a table.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Create a clustered index on (CustomerID, OrderDate) and a non-clustered index on OrderID.
Why it's wrong here
Incorrect. A clustered index on (CustomerID, OrderDate) would make single order lookups by OrderID slower because the clustered index determines the physical order, and OrderID lookups would require a key lookup or a non-clustered index scan.
- ✓
B. Create a clustered index on OrderID and a non-clustered index on (CustomerID, OrderDate).
Why this is correct
Correct. The clustered index on OrderID optimizes single row lookups. The non-clustered index on (CustomerID, OrderDate) supports range queries filtering on those columns. This combination provides optimal performance for both query patterns.
Clue confirmation
The clue words "best", "primary" in the question point toward this answer.
Related concept
A clustered index determines the physical storage order of data rows in a table.
- ✗
C. Create a non-clustered index on each column used in WHERE clauses: OrderID, CustomerID, and OrderDate.
Why it's wrong here
Incorrect. While individual indexes can be used, they are less efficient for composite range queries. A single composite index on (CustomerID, OrderDate) would better support the range condition and avoid index intersections.
- ✗
D. Create a clustered columnstore index on the entire table and no other indexes.
Why it's wrong here
Incorrect. Columnstore indexes are designed for analytical workloads and large scans, not for frequent singleton lookups or point queries. They would degrade performance for the transactional queries described.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume the clustered index must match the most frequent query pattern, but they overlook that a clustered index on a non-unique column like CustomerID can cause fragmentation and slower point lookups, whereas the correct design separates the point-lookup and range-query concerns into two distinct indexes.
Detailed technical explanation
How to think about this question
In Azure SQL Database, a clustered index on OrderID stores the actual data rows in B-tree order by OrderID, making equality lookups extremely fast via a single index seek. The non-clustered index on (CustomerID, OrderDate) can be created as a covering index by including the TotalAmount column (e.g., using INCLUDE) to avoid key lookups entirely, further optimizing the frequent range query. This dual-index strategy balances OLTP and reporting needs without sacrificing performance.
KKey Concepts to Remember
- A clustered index determines the physical storage order of data rows in a table.
- There can only be one clustered index per table.
- Non-clustered indexes are separate structures containing indexed columns and pointers to data rows.
- A composite non-clustered index can efficiently support queries filtering and ordering on multiple columns.
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
A clustered index determines the physical storage order of data rows in a table.
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. A clustered index determines the physical storage order of data rows in a table. 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.
Review a clustered index determines the physical storage order of data rows in a table., then practise related DP-900 questions on the same topic to reinforce the concept.
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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 — A clustered index determines the physical storage order of data rows in a table..
What is the correct answer to this question?
The correct answer is: B. Create a clustered index on OrderID and a non-clustered index on (CustomerID, OrderDate). — Option B is correct because a clustered index on OrderID ensures fast single-row lookups by the primary key, while a non-clustered index on (CustomerID, OrderDate) efficiently covers the range query filtering by CustomerID and ordering by OrderDate. This design leverages the clustered index for point lookups and the non-clustered index as a covering index for the most frequent query pattern.
What should I do if I get this DP-900 question wrong?
Review a clustered index determines the physical storage order of data rows in a table., then practise related DP-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best", "primary". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
A clustered index determines the physical storage order of data rows in a table.
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Last reviewed: Jun 11, 2026
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