DP-300 Covering Index Practice Question
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?
⚠ Common exam trap
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.
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
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.
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.
Go deeper
Related to this question
Learn chapter
Overview of Azure Data Platform Options
Key term
Azure SQL Indexes
Structures in Azure SQL Database that speed up data retrieval by providing quick access paths to rows, similar to a book index.
Key term
Azure SQL Performance Tuning
Azure SQL Performance Tuning is the process of optimizing the speed and efficiency of queries and database operations in Microsoft Azure SQL Database or SQL Managed Instance to reduce latency and improve throughput.
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This DP-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-300 exam.