DP-300 Practice Question: Monitor, configure, and optimize database resources
A production Azure SQL Database is experiencing high CPU usage during peak hours. The database uses the S3 service tier. You need to reduce CPU usage without changing the service tier. Which action should you take?
⚠ Common exam trap
Many exam-takers assume reducing MAXDOP or increasing workers will fix CPU issues, but without addressing the root cause (poor query plans from missing indexes), these changes either exacerbate resource contention or fail to reduce CPU usage.
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
✓
Identify and create missing indexes.
High CPU usage in an S3 Azure SQL Database often stems from inefficient query plans caused by missing indexes. Creating appropriate indexes reduces the number of rows scanned and the CPU cycles needed for operations like key lookups and sorting, directly lowering CPU consumption without changing the service tier.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum number of concurrent workers.
Why it's wrong here
Raising max concurrent workers lets more requests queue for CPU, worsening contention rather than lowering usage. It tempts as a throughput tuning knob, and would be correct when sessions are blocked waiting on workers, not when CPU is the saturated bottleneck.
- ✓
Identify and create missing indexes.
Why this is correct
Missing indexes force full table or index scans, so creating them lets the query optimiser seek directly to matching rows, cutting CPU consumed per query. This satisfies the constraint of reducing CPU usage while remaining on the S3 service tier.
- ✗
Reduce MAXDOP to 1.
Why it's wrong here
Forcing MAXDOP to 1 serialises parallel queries, which can raise CPU duration and runtime for large scans. It tempts as a fix for parallelism-related waits, and would be correct when CXPACKET or excessive parallelism, not raw CPU saturation, drives the problem.
- ✗
Increase MAXDOP to 8.
Why it's wrong here
Raising MAXDOP to 8 widens parallelism, consuming more worker threads and CPU, not less. It tempts because parallelism speeds individual queries, and would be correct when a single long-running query underuses available cores rather than when aggregate CPU is already saturated.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
Learn chapter
Optimizing Database Query and Index Performance
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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