DP-300 Practice Question: Monitor, configure, and optimize database resources
A company has an Azure SQL Database that is experiencing performance degradation during peak hours. The database is configured with the Standard tier (S2). Which action should you recommend to improve performance without changing the application code?
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 objective (e.g., S3).
Scaling up to a higher service objective (e.g., S3) increases DTUs, providing more resources to handle peak loads without requiring any application code changes. Option B is wrong: Query Store aids in performance monitoring and troubleshooting but does not directly improve performance. Option C is wrong: read scale-out offloads read-only workloads to a readable secondary, but the issue is general performance degradation, not specifically read-heavy. Option D is wrong: creating nonclustered indexes may improve query performance but often requires application or query adjustments, and does not guarantee improvement without code changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Scale up the database to a higher service objective (e.g., S3).
Why this is correct
Increases DTU limit, providing more resources.
- ✗
Enable Query Store and run the Performance Dashboard.
Why it's wrong here
Only monitors performance, does not fix it.
- ✗
Enable read scale-out to offload read queries.
Why it's wrong here
Not applicable for Standard tier; only for Premium/Business Critical.
- ✗
Create nonclustered indexes on all tables.
Why it's wrong here
Requires application code changes and index design.
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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