DP-900 Practice Question: Identify considerations for relational data on Azure
A company runs an e-commerce application on Azure SQL Database. The application experiences unpredictable traffic spikes during flash sales and promotional events. The company wants to automatically scale compute resources based on actual demand and pay only for the resources consumed. Which Azure SQL Database deployment option best meets these requirements?
⚠ Common exam trap
Candidates often confuse Hyperscale's storage scalability with compute auto-scaling, or assume Provisioned tiers can automatically scale without manual intervention, when in fact only Serverless provides automatic compute scaling and per-second billing for intermittent workloads.
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
The Serverless deployment option for Azure SQL Database automatically scales compute resources (vCores) based on actual demand, pausing the database during idle periods and resuming on the first connection. This model charges per second for the compute used, making it ideal for unpredictable traffic spikes like flash sales, as it eliminates the need to over-provision and ensures you pay only for consumed resources.
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
Why this is correct
Serverless is the correct compute tier for intermittent, unpredictable workloads because it automatically scales compute resources (in vCores) based on actual demand and can pause the database during idle periods, billing only for storage while paused. With a configurable auto-pause delay (1 to 60 minutes) and per-second billing during active use, it eliminates the need to manually resize compute. This makes it far more cost-effective than maintaining fixed compute capacity that would sit idle most of the time, which is exactly why it suits an e-commerce application with spikes in traffic.
- ✗
Provisioned DTU
Why it's wrong here
The Provisioned DTU purchasing model offers fixed compute tiers (Basic, Standard, Premium) that allocate a set number of DTUs (a blending of CPU, memory, and I/O). You must manually scale up or down by changing service tiers, and the database never automatically pauses or scales in response to demand, so you pay continuously for the provisioned capacity. For intermittent unpredictable workloads, this would require over-provisioning to handle peaks, leaving you paying for idle capacity during quiet times, making it a poor fit compared to Serverless.
- ✗
Provisioned vCore
Why it's wrong here
Provisioned vCore compute (under the vCore-based purchasing model) gives you explicit control over the number of vCores and memory, but requires you to manually select and resize the compute allocation; there is no automatic scaling or pausing built in. You are billed for the provisioned vCores for every second regardless of whether the database is actively used, so it incentivizes continuous uptime and steady workloads. An e-commerce app with unpredictable bursts would force you to either pay for peak capacity at all times or risk performance degradation during sudden spikes, which Serverless avoids.
- ✗
Hyperscale
Why it's wrong here
Hyperscale is an architecture designed for very large databases (up to 100 TB) that emphasizes fast horizontal scaling of compute replicas, high availability, and near-instant backup/restore—not automatic pausing of compute. It does allow you to add or remove compute replicas, but you must always keep at least the primary replica running, so there is no auto-pause and you incur continuous compute costs. For a small e-commerce app with intermittent unpredictable usage, Hyperscale is significantly over-provisioned and more expensive; it doesn't address the same cost optimization problem as Serverless.
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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