AZ-305 Design infrastructure solutions Practice Question
Your company has a web application deployed on Azure App Service that experiences periodic traffic spikes. You need to ensure the application scales out quickly without manual intervention. The solution must minimize cost during low-traffic periods. What should you configure?
⚠ Common exam trap
Candidates often confuse pre-warming (Option A) with autoscaling, thinking it handles scaling automatically, but pre-warming only prepares instances for a planned scale event and does not react to real-time metrics.
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
✓
Configure Autoscale rules based on CPU or memory metrics
Autoscale rules based on CPU or memory metrics allow the App Service to automatically increase or decrease the number of instances in response to traffic spikes, meeting the requirement for quick scaling without manual intervention. By scaling down during low-traffic periods, it minimizes cost, as you only pay for the instances you use. This is the native Azure App Service scaling feature that directly addresses the need for elastic, cost-effective scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable pre-warming in the App Service plan
Why it's wrong here
Enabling pre-warming in the App Service plan does not scale the number of running instances; it only initializes application code on a new instance before traffic is routed to it. This feature is typically used with Always On or deployment slots to reduce cold-start latency, but it offers no automatic adjustment of instance counts based on load or metrics. Therefore, it cannot solve a need for dynamic scaling under varying CPU or memory pressure.
- ✓
Configure Autoscale rules based on CPU or memory metrics
Why this is correct
Configuring autoscale rules is the proper PaaS-native mechanism for Azure App Service to automatically adjust the instance count of your App Service plan. You define scale conditions based on metrics such as CPU percentage, memory consumption, or request queue length, and Autoscale adds or removes instances in response to those thresholds. This is a fully automated, reactive approach that continuously monitors load and eliminates the need for manual intervention.
- ✗
Deploy the app to a Virtual Machine Scale Set
Why it's wrong here
Deploying the application to a Virtual Machine Scale Set is incorrect because the application is already hosted on Azure App Service, a PaaS offering. Migrating to a VMSS would require rebuilding the application as a VM image, implementing update/domain management, patching, and configuring a separate load balancer—none of which are necessary for App Service. Moreover, VMSS autoscaling operates at the virtual machine level, not at the App Service level, so it cannot scale the existing App Service plan.
- ✗
Manually increase the instance count before expected spikes
Why it's wrong here
Manually increasing the instance count before expected spikes requires you to predict traffic and intervene by modifying the scale-out settings in the Azure portal or via CLI/scripts. This approach is error-prone because it doesn't react to actual real-time metrics; if the spike is larger or earlier than expected, the app may still be under-provisioned, and if traffic is lower, you waste resources. Autoscale handles this dynamically, so manual scaling is unnecessary and inferior for demand-driven workloads.
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