- A
Create a clustered columnstore index on the table
Why wrong: Creating a clustered columnstore index is wrong because it is optimized for analytical queries and can degrade point lookup performance.
- B
Create a filtered index on customer ID for frequent values
Why wrong: Creating a filtered index on customer ID for frequent values is wrong because it still does not cover all queried columns, leading to key lookups for other columns.
- C
Partition the table by customer ID
Why wrong: Partitioning the table by customer ID is wrong because it does not eliminate key lookups and may not reduce IO for point queries.
- D
Add all queried columns as included columns to the nonclustered index
Adding all queried columns as included columns to the nonclustered index makes it covering, eliminating key lookups and reducing IO with minimal storage overhead.
DP-300 Covering Index Practice Question
This DP-300 practice question tests your understanding of monitor, configure, and optimize database resources. 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: covering Index. 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.
You administer a large Azure SQL Database that is used for a SaaS application. The database has a table with over 1 billion rows that is frequently queried by customer ID. The table currently has a clustered index on an identity column and a nonclustered index on customer ID. Queries that filter by customer ID are experiencing high IO and long execution times. You analyze the execution plan and see that the nonclustered index is used, but there are many key lookups. You need to optimize the query performance while minimizing storage overhead. What should you do?
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
Add all queried columns as included columns to the nonclustered index
Option D is correct because adding all queried columns as included columns to the existing nonclustered index on customer ID creates a covering index. This eliminates the need for key lookups, reducing IO and improving query performance for point lookups by customer ID. The storage overhead is minimal since included columns are stored only at the leaf level. Option A is wrong because a clustered columnstore index is designed for analytical workloads and can degrade point lookup performance. Option B is wrong because a filtered index on frequent values still may not cover all columns, leading to key lookups. Option C is wrong because partitioning does not eliminate key lookups and can add complexity without performance benefit for point queries.
Key principle: Covering Index
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 it's wrong here
Creating a clustered columnstore index is wrong because it is optimized for analytical queries and can degrade point lookup performance.
- ✗
Create a filtered index on customer ID for frequent values
Why it's wrong here
Creating a filtered index on customer ID for frequent values is wrong because it still does not cover all queried columns, leading to key lookups for other columns.
- ✗
Partition the table by customer ID
Why it's wrong here
Partitioning the table by customer ID is wrong because it does not eliminate key lookups and may not reduce IO for point queries.
- ✓
Add all queried columns as included columns to the nonclustered index
Why this is correct
Adding all queried columns as included columns to the nonclustered index makes it covering, eliminating key lookups and reducing IO with minimal storage overhead.
Related concept
Covering Index
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that clustered columnstore indexes are often suggested for large tables to reduce storage and improve IO, but they are optimized for analytic workloads, not high-frequency point lookups. For point lookup queries, a covering nonclustered index is a better choice.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Covering Index
- Clustered Columnstore Index
- Key Lookup
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
Covering Index
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review covering Index, then practise related DP-300 questions on the same topic to reinforce the concept.
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Monitor, configure, and optimize database resources — study guide chapter
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Monitor, configure, and optimize database resources practice questions
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FAQ
Questions learners often ask
What does this DP-300 question test?
Monitor, configure, and optimize database resources — This question tests Monitor, configure, and optimize database resources — Covering Index.
What is the correct answer to this question?
The correct answer is: Add all queried columns as included columns to the nonclustered index — Option D is correct because adding all queried columns as included columns to the existing nonclustered index on customer ID creates a covering index. This eliminates the need for key lookups, reducing IO and improving query performance for point lookups by customer ID. The storage overhead is minimal since included columns are stored only at the leaf level. Option A is wrong because a clustered columnstore index is designed for analytical workloads and can degrade point lookup performance. Option B is wrong because a filtered index on frequent values still may not cover all columns, leading to key lookups. Option C is wrong because partitioning does not eliminate key lookups and can add complexity without performance benefit for point queries.
What should I do if I get this DP-300 question wrong?
Review covering Index, then practise related DP-300 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Covering Index
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Last reviewed: Jun 21, 2026
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