DP-300 Practice Question: Monitor, configure, and optimize database resources
You are optimizing an Azure SQL Database that runs a heavy reporting workload. The database uses the General Purpose tier. You notice that many queries are scanning large tables. What is the best first action to improve performance?
⚠ Common exam trap
The trap is assuming that scaling up (more hardware) or partitioning (structural change) is the best first step, when the exam expects you to identify the least invasive, data-driven action — analyzing existing recommendations before making costly changes.
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
✓
Analyze the missing index recommendations from Query Store.
Query Store's missing index recommendations directly identify indexes that, if created, would likely improve the performance of the observed scanning queries. This is the most targeted, low-risk first action because it is based on actual workload data and addresses the root cause (missing indexes causing scans) without changing the service tier or making structural changes. It is also the cheapest and fastest to implement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition the large tables by date.
Why it's wrong here
Partitioning splits data across filegroups but does not eliminate the full scans; the reporting queries still read every row of each partition. Partitioning suits maintenance operations like sliding-window archival, not scan reduction, so it addresses a different problem than the stem describes.
- ✓
Analyze the missing index recommendations from Query Store.
Why this is correct
Query Store captures missing index recommendations from actual workload history, letting you add indexes that eliminate the large table scans. This directly targets the scan bottleneck and is the cheapest first action, unlike scaling the General Purpose tier or rewriting queries blindly.
- ✗
Scale up to Business Critical tier.
Why it's wrong here
Business Critical adds local SSD and a readable secondary, yet scans remain scans; the storage tier does not change the access method. It is tempting because Business Critical targets latency-sensitive workloads, but the stem's bottleneck is scan volume, which columnstore indexing addresses.
- ✗
Implement columnstore indexes on all large tables.
Why it's wrong here
Columnstore indexes accelerate analytical scans, but the General Purpose tier's remote Premium Azure Storage adds latency that dominates a heavy reporting workload. Columnstore indexing becomes the right first step once the database runs on Business Critical, where local SSD storage removes that I/O bottleneck.
Go deeper
Related to this question
Learn chapter
Optimizing Database Query and Index Performance
Key term
Query Store
Query Store is a built-in SQL Server feature that captures and stores a history of query execution plans and performance data for easy monitoring and troubleshooting.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.