AZ-204 Develop Azure compute solutions Practice Question
You have an Azure App Service web app that experiences high CPU usage during peak hours. You need to scale out automatically based on CPU load. What should you configure?
⚠ Common exam trap
Test-takers frequently confuse 'scale up' (changing the plan tier) with 'scale out' (adding instances), and may incorrectly select Option B thinking it addresses CPU load, but scaling up does not increase instance count.
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 an autoscale rule to scale out based on CPU percentage.
Azure App Service autoscale rules allow you to scale out (increase instance count) based on a metric like CPU percentage. This automatically adds more instances when CPU exceeds a threshold, distributing the load and reducing CPU usage per instance during peak hours.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually increase the instance count during peak hours.
Why it's wrong here
While manually increasing instance count can address high load, it is a reactive and non-automated approach. This method requires constant monitoring and human intervention, making it inefficient and prone to delays in responding to sudden or unpredictable spikes in demand. It does not provide the dynamic, real-time scaling necessary to maintain consistent performance and cost-efficiency for an application experiencing high variability, which is the core problem autoscale solves.
- ✗
Configure an autoscale rule to scale up the App Service plan.
Why it's wrong here
Configuring an autoscale rule to "scale up" the App Service plan would increase the computational resources (CPU, memory) available to each existing instance of the web app. While this can improve performance for a single instance, it does not address the need to handle increased concurrent user load by adding more instances. High traffic typically requires horizontal scaling (scaling out) to distribute the load across multiple application instances, rather than just making existing ones more powerful.
- ✓
Configure an autoscale rule to scale out based on CPU percentage.
Why this is correct
Configuring an autoscale rule to "scale out" based on CPU percentage is the most effective solution for an Azure App Service web app experiencing high variability. Scaling out dynamically adds more instances of the web app to the App Service plan when the average CPU utilization across existing instances exceeds a defined threshold. This horizontal scaling distributes the incoming load across multiple instances, ensuring consistent performance and responsiveness during peak demand without manual intervention.
- ✗
Use Azure Front Door to distribute load across multiple instances.
Why it's wrong here
Azure Front Door is a global, scalable entry-point that can distribute traffic across multiple backend origins, providing features like Web Application Firewall (WAF), CDN, and global load balancing. However, Front Door itself does not automatically scale the number of instances of the backend Azure App Service. It can direct traffic to existing instances and improve overall delivery, but it relies on the App Service's own autoscale rules to dynamically provision or deprovision those instances in response to load.
Go deeper
Related to this question
About these practice questions
One of 881 original AZ-204 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-204 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-204 exam.