AZ-204 Develop Azure compute solutions Practice Question
Your company has a multi-tier application running on Azure Virtual Machines. The application experiences high CPU usage during peak hours. You need to implement autoscaling for the virtual machine scale set based on CPU usage. The scaling should be aggressive when CPU exceeds 80% and conservative when CPU drops below 30%. Which scaling rule configuration should you use?
⚠ Common exam trap
Watch out — candidates often choose Option C because it seems 'balanced' with a single threshold, not realizing that identical scale-out and scale-in thresholds cause autoscale flapping, and that the question explicitly demands distinct aggressive (80%) and conservative (30%) values.
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
✓
Scale out when CPU > 80%, scale in when CPU < 30%
The question explicitly requires aggressive scaling when CPU exceeds 80% and conservative scaling when CPU drops below 30%. The scale-out threshold of 80% triggers rapid addition of instances to handle high load, while the scale-in threshold of 30% ensures instances are removed only when utilization is consistently low, preventing premature scale-in and thrashing. This matches the exact thresholds specified in the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale out when CPU > 50%, scale in when CPU < 20%
Why it's wrong here
A scale-out threshold of 50% CPU utilization is often too aggressive, leading to premature scaling and increased operational costs even during moderate load fluctuations. While a 20% scale-in threshold aims to conserve resources, the relatively small buffer between scale-out and scale-in could still result in frequent scaling actions, potentially causing 'autoscale thrashing' if not carefully managed.
- ✗
Scale out when CPU > 90%, scale in when CPU < 10%
Why it's wrong here
Setting the scale-out threshold at 90% CPU utilization is typically too high, as it allows the application to experience significant performance degradation and user-perceived latency before new instances are provisioned. Conversely, a scale-in threshold of 10% CPU is overly conservative, keeping idle resources running for extended periods. This configuration leads to unnecessary cloud expenditure by failing to de-provision underutilized instances promptly.
- ✗
Scale out when CPU > 70%, scale in when CPU < 70%
Why it's wrong here
Configuring identical or very close scale-out and scale-in thresholds, such as 70% for both, is highly problematic and a common misconfiguration. This setup almost guarantees 'autoscale thrashing,' where instances are rapidly added and removed as CPU utilization fluctuates around the threshold. Such instability severely impacts application performance, increases operational complexity, and can lead to higher costs due to frequent resource provisioning and de-provisioning.
- ✓
Scale out when CPU > 80%, scale in when CPU < 30%
Why this is correct
A scale-out threshold of 80% CPU utilization provides an optimal balance, ensuring that new instances are added proactively to maintain performance without over-provisioning for transient spikes. The scale-in threshold of 30% CPU creates a sufficient buffer, preventing premature de-provisioning of instances when demand temporarily dips. This significant delta between thresholds is crucial for maintaining system stability, preventing 'autoscale thrashing,' and optimizing both application responsiveness and cloud costs.
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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.