DP-300 Practice Question: Monitor, configure, and optimize database resources
You manage an Azure SQL Database that runs a customer-facing application. You notice that during peak hours, the DTU consumption reaches 100% and queries experience timeouts. You need to ensure consistent performance without manual intervention. What should you implement?
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
✓
Scale up the database to a higher service tier (e.g., from S2 to S3).
Scaling up to a higher service tier (e.g., from S2 to S3) increases DTU limits, providing more CPU, memory, and IO resources to handle peak demand, thus preventing timeouts without manual intervention. Option A is incorrect because Query Store helps monitor and troubleshoot performance but does not automatically resolve resource shortages. Option B is incorrect because read scale-out offloads read-only queries to a read-only replica, but the issue is DTU consumption reaching 100%, which affects all queries, not just reads. Option C is incorrect because automatic tuning can fix plan regression but does not add resources; it only optimizes query plans.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Query Store to identify costly queries.
Why it's wrong here
Query Store monitors but does not automatically resolve resource shortages.
- ✗
Implement read scale-out to offload read queries.
Why it's wrong here
Read scale-out does not increase DTU capacity; it only distributes read-only workloads.
- ✗
Configure automatic tuning to force plan regression fixes.
Why it's wrong here
Automatic tuning addresses query plan issues, not DTU exhaustion.
- ✓
Scale up the database to a higher service tier (e.g., from S2 to S3).
Why this is correct
Scaling up increases DTU limits, alleviating resource contention.
Visual reference
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
Overview of Azure Data Platform Options
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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