- A
Enable 'Always On' and configure manual scale based on scheduled times.
Why wrong: 'Always On' keeps the app loaded but does not scale automatically. Manual scale cannot adapt to real-time load fluctuations.
- B
Configure autoscale rules on the App Service plan scale-out setting, using CPU percentage as the metric with appropriate thresholds and cool-down periods.
This is the standard approach. In the Azure portal, under the App Service plan's 'Scale out' (App Service plan settings), you can add autoscale conditions with rules based on CPU percentage.
- C
Use Azure Functions with the Consumption Plan to handle the web app logic.
Why wrong: Azure Functions is not a direct replacement for a web app. It is for event-driven, short-lived functions, not for hosting a full web application with a UI and state.
- D
Deploy the web app to Azure Container Instances and use the scale-on-CPU feature.
Why wrong: Azure Container Instances does not have built-in autoscaling based on CPU. It's for single container deployments; scaling is manual or requires a container orchestrator like Kubernetes.
Quick Answer
The answer is to configure autoscale rules on the App Service plan scale-out setting using CPU percentage as the metric with appropriate thresholds and cool-down periods. This is correct because Azure App Service autoscale allows you to define a scale-out rule that triggers when CPU usage exceeds 75% and a scale-in rule when it drops below 25%, while cool-down periods (typically 5–10 minutes) ensure gradual scaling by preventing rapid, repeated scaling actions that cause thrashing. On the AZ-204 exam, this scenario tests your understanding of scaling policies within App Service plans, often appearing as a scenario where you must distinguish between rule-based autoscale and manual scaling or Azure Functions scale controllers. A common trap is forgetting to set cool-down periods, leading to instability, or confusing the metric source (App Service plan vs. individual app). Memory tip: think “75 out, 25 in, cool down to win” — the thresholds and a pause prevent the yo-yo effect.
AZ-204 Develop Azure compute solutions Practice Question
This AZ-204 practice question tests your understanding of develop azure compute solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You have an Azure App Service web app that experiences fluctuating traffic. During peak hours, the CPU usage reaches 90% and response times increase. You want to automatically scale out the number of instances when CPU usage exceeds 75% and scale in when it drops below 25%. The scaling should be gradual to avoid thrashing. Which configuration should you use?
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 on the App Service plan scale-out setting, using CPU percentage as the metric with appropriate thresholds and cool-down periods.
Option B is correct because Azure App Service autoscale rules allow you to scale out (increase instance count) when CPU percentage exceeds 75% and scale in (decrease instance count) when it drops below 25%, with configurable cool-down periods (e.g., 5–10 minutes) to prevent thrashing. This directly addresses the fluctuating traffic pattern and gradual scaling requirement using the App Service plan's scale-out blade.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 'Always On' and configure manual scale based on scheduled times.
Why it's wrong here
'Always On' keeps the app loaded but does not scale automatically. Manual scale cannot adapt to real-time load fluctuations.
- ✓
Configure autoscale rules on the App Service plan scale-out setting, using CPU percentage as the metric with appropriate thresholds and cool-down periods.
Why this is correct
This is the standard approach. In the Azure portal, under the App Service plan's 'Scale out' (App Service plan settings), you can add autoscale conditions with rules based on CPU percentage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Azure Functions with the Consumption Plan to handle the web app logic.
Why it's wrong here
Azure Functions is not a direct replacement for a web app. It is for event-driven, short-lived functions, not for hosting a full web application with a UI and state.
- ✗
Deploy the web app to Azure Container Instances and use the scale-on-CPU feature.
Why it's wrong here
Azure Container Instances does not have built-in autoscaling based on CPU. It's for single container deployments; scaling is manual or requires a container orchestrator like Kubernetes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'Always On' (which keeps the app warm) with autoscaling, or mistakenly think Azure Functions or Container Instances are drop-in replacements for App Service autoscale, ignoring the specific requirements for gradual, metric-based scaling with cool-down periods.
Detailed technical explanation
How to think about this question
Autoscale in Azure App Service uses Azure Monitor metrics (e.g., CPU percentage) and evaluates them at 1-minute intervals, applying scale rules only after the cool-down period expires (default 5 minutes for scale-out, 10 minutes for scale-in). This prevents rapid oscillation (thrashing) by ensuring the metric remains above or below the threshold for a sustained duration before triggering another scaling operation. In a real-world scenario, you might also set a maximum instance limit (e.g., 10) to control cost and avoid runaway scaling.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Develop Azure compute solutions — This question tests Develop Azure compute solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure autoscale rules on the App Service plan scale-out setting, using CPU percentage as the metric with appropriate thresholds and cool-down periods. — Option B is correct because Azure App Service autoscale rules allow you to scale out (increase instance count) when CPU percentage exceeds 75% and scale in (decrease instance count) when it drops below 25%, with configurable cool-down periods (e.g., 5–10 minutes) to prevent thrashing. This directly addresses the fluctuating traffic pattern and gradual scaling requirement using the App Service plan's scale-out blade.
What should I do if I get this AZ-204 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AZ-204
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Manually increase the instance count during peak hours.
- B.Configure an autoscale rule to scale up the App Service plan.
- ✓ C.Configure an autoscale rule to scale out based on CPU percentage.
- D.Use Azure Front Door to distribute load across multiple instances.
Why C: Option C is correct because 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.
Variation 2. 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. Which scaling solution should you use?
medium- A.Manual scaling
- B.Traffic Manager
- ✓ C.Autoscale with a CPU percentage rule
- D.Scale sets autoscale
Why C: Autoscale with a CPU percentage rule is the correct solution because Azure App Service supports built-in autoscaling that can automatically increase or decrease the number of instances based on a metric like CPU percentage. This allows the web app to handle peak-hour traffic by scaling out when CPU usage exceeds a defined threshold, ensuring performance without manual intervention.
Variation 3. Your Azure App Service web app experiences slow response times during peak hours. You suspect the App Service plan is under-provisioned. You need to scale out the app automatically based on CPU usage. What should you configure?
medium- A.Configure scheduled scaling in the app
- B.Manually increase instance count
- C.Configure scale up in the App Service plan
- ✓ D.Configure autoscale rules in the App Service plan
Why D: Autoscale rules in the App Service plan allow you to automatically scale out (increase instance count) based on metrics like CPU usage. This is the correct solution because it dynamically adjusts capacity in response to demand during peak hours without manual intervention.
Last reviewed: Jun 11, 2026
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.
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