DP-900 Practice Question: Identify considerations for relational data on Azure
A company runs a web application on Azure SQL Database that experiences unpredictable spikes in traffic. They want to automatically adjust compute resources based on demand without manual intervention and without over-provisioning. Which Azure SQL Database feature should they use?
⚠ Common exam trap
Candidates often confuse the Hyperscale service tier's storage scalability with compute auto-scaling, but Hyperscale requires manual vCore adjustment and does not support auto-pause, whereas Serverless is specifically designed for unpredictable, intermittent workloads with automatic compute scaling.
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
✓
Serverless compute tier
The Serverless compute tier for Azure SQL Database automatically scales compute resources based on workload demand, pausing databases during idle periods and resuming them when traffic spikes occur. This eliminates the need for manual intervention and prevents over-provisioning by charging only for the compute used per second, making it ideal for unpredictable traffic patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Serverless compute tier
Why this is correct
Serverless compute tier automatically scales compute resources based on actual demand, scaling down or even pausing the database during periods of inactivity to control costs. It bills per-second for compute and storage separately, making it ideal for workloads with unpredictable, intermittent spikes where manual provisioning or pre-scaling would be wasteful. This meets the requirement because it handles those spikes without intervention.
- ✗
Active geo-replication
Why it's wrong here
Active geo-replication is a disaster recovery feature that maintains readable secondary replicas of a database in different Azure regions, allowing failover and read access for reporting. It does not dynamically adjust compute capacity on the primary database; instead, it replicates data to separate logical servers that are provisioned with their own compute and storage. Therefore, it cannot address unpredictable compute spikes within the primary's own tier.
- ✗
Hyperscale service tier
Why it's wrong here
Hyperscale is a service tier for very large databases, supporting up to 100 TB of storage and providing rapid scaling of compute by adding or removing multiple read-scale replicas. However, it operates in a provisioned compute model: you must choose the number of compute replicas and their size, and it does not automatically pause during inactivity or scale to zero like Serverless. Thus it offers fast vertical/horizontal scaling but not the fully automated, demand-driven compute management needed for unpredictable spikes.
- ✗
Read scale-out
Why it's wrong here
Read scale-out is a feature that routes read-only connections to a built-in read-only replica of the database to offload analytic or reporting queries without impacting the read-write workload. This improves query performance for reporting but has no effect on the compute resources allocated to the primary database or its ability to handle write spikes. It is not a scaling mechanism for the primary's compute or automatically adjusting capacity based on demand.
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 |
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