DP-300 Plan and implement data platform resources Practice Question
You are a database administrator for a company that runs a SaaS application on Azure SQL Database. The application's workload is unpredictable, with rapid bursts of activity that last only a few minutes. You need to ensure that the database can handle these bursts without manual intervention, while minimizing cost during idle periods. What should you do?
⚠ Common exam trap
The trap here is assuming that any auto-scaling feature, such as read scale-out or elastic pools, will handle unpredictable compute bursts for a single database.
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
✓
Enable the serverless compute tier for the database.
The serverless compute tier is specifically designed for single databases with unpredictable or intermittent usage. It automatically scales compute based on demand and pauses during inactivity, which directly addresses the need to handle bursts without manual intervention while minimizing cost. Other options either require manual scaling or are optimized for different scenarios such as large databases or read offloading.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the database to use the Hyperscale service tier with a fixed number of vCores.
Why it's wrong here
Hyperscale is designed for large databases (up to 100 TB) and provides fast scale-out for read workloads, but it does not automatically adjust compute resources based on workload demand. A fixed number of vCores would not handle bursts without manual scaling, and it would incur cost during idle periods. This option does not meet the requirement for automatic scaling.
- ✓
Enable the serverless compute tier for the database.
Why this is correct
The serverless compute tier automatically scales compute resources based on workload demand and pauses the database during inactive periods, billing only for storage. This directly addresses unpredictable bursts by scaling up during activity and scaling down or pausing when idle, minimizing cost. It requires no manual intervention and is ideal for intermittent, unpredictable workloads.
- ✗
Create a read scale-out replica and route all write operations to the primary replica.
Why it's wrong here
Read scale-out replicas are used to offload read-only workloads from the primary database, improving read performance. However, they do not automatically scale compute resources for write-heavy bursts. The primary replica would still need to handle all writes, and manual scaling would be required to manage bursts, which does not meet the requirement for automatic handling.
- ✗
Implement elastic database pool with multiple databases sharing resources.
Why it's wrong here
Elastic pools are designed for multiple databases with varying usage patterns, allowing resource sharing among them. However, for a single database with unpredictable bursts, an elastic pool does not provide automatic scaling of compute resources per database. It requires manual configuration of pool resources and does not pause during idle periods, so it may not minimize cost effectively.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Implementing High Availability for Azure SQL Databases
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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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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