DP-900 Practice Question: Identify considerations for relational data on Azure
A company runs an e-commerce application on Azure SQL Database. During seasonal promotions, traffic spikes significantly, but at other times traffic is low. They want to automatically adjust compute resources based on demand without manual intervention or provisioning. Which Azure SQL Database feature should they use?
⚠ Common exam trap
Many candidates confuse Elastic pools (which scale shared resources across multiple databases) with the single-database auto-scaling behavior of Serverless compute, or they assume Hyperscale's high scalability automatically includes dynamic compute scaling without manual intervention.
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
Serverless compute for Azure SQL Database automatically scales compute resources based on workload demand and pauses the database during inactive periods, charging only for storage and compute used per second. This matches the requirement for automatic adjustment without manual intervention or provisioning, especially for intermittent, unpredictable traffic spikes like seasonal promotions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Geo-replication
Why it's wrong here
Geo-replication in Azure SQL Database continuously replicates data from a primary database to a secondary in a different Azure region, primarily for business continuity and disaster recovery. While the secondary can be used for read-only queries, it does not automatically resize or adjust compute based on live demand. The replication is asynchronous and oriented toward failover, not toward elastic scaling of the primary's compute resources.
- ✗
Elastic pools
Why it's wrong here
Elastic pools are a cost-management feature that pools vCores or DTUs shared across multiple databases with per-database performance limits. The pool's total size is manually configured by an administrator, and although individual databases can use more resources up to a cap, the pool itself does not automatically scale based on variable application load. For a single database that needs automatic, demand-driven compute scaling, serverless compute is the correct choice.
- ✓
Serverless compute
Why this is correct
Azure SQL Database serverless automatically scales the compute capacity between a configured minimum and maximum number of vCores based on actual workload demand, and it pauses the database when idle to eliminate compute billing. This makes it ideal for intermittent or unpredictable workloads, as it requires no manual intervention and computes billing per second. Note that serverless does not auto-scale storage; storage is billed separately and remains available even while paused.
- ✗
Hyperscale
Why it's wrong here
Hyperscale is a service tier designed for databases with very large storage requirements, high throughput, and support for rapid scaling of storage and multiple readable replicas. It offers horizontal scale-out of compute nodes for reads, but compute scaling is not automatically driven by demand as in serverless; you explicitly manage replicas and the primary compute. Hyperscale is therefore better suited for sustained, performance-intensive production workloads rather than the intermittent, bursty, cost-conscious pattern described in the scenario.
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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