VMSS Autoscale Based on CPU Usage for Identical Web VMs
You need to deploy 20 identical Azure virtual machines that host the same web application. The solution must support automatic scale-out based on CPU usage and should minimize administrative overhead. What should you deploy?
Quick Answer
The answer is a Virtual Machine Scale Set (VMSS). This is the correct choice because VMSS is specifically designed to deploy and manage a group of identical, load-balanced VMs as a single resource, and it natively supports autoscale policies that trigger scale-out based on metrics like average CPU usage across all instances. On the AZ-104 exam, this scenario tests your understanding of when to choose VMSS over individual VMs or an availability set—the key trap is selecting individual VMs with a separate autoscale solution, which increases administrative overhead unnecessarily. Remember that VMSS minimizes overhead by handling VM creation, scaling rules, and load balancing together, making it ideal for identical web workloads. A quick memory tip: think "identical + autoscale = VMSS," and recall that the "S" in VMSS stands for "Scale Set," directly linking it to the scaling requirement.
⚠ Common exam trap
Candidates often confuse an availability set (which provides high availability but no scaling) with a scale set (which provides both scaling and high availability), or they mistakenly think deploying individual VMs in a resource group is simpler, ignoring the requirement for automatic scale-out and reduced administrative overhead.
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
✓
A Virtual Machine Scale Set.
A Virtual Machine Scale Set (VMSS) is the correct choice because it automates the deployment and management of identical VMs, supports autoscaling based on CPU usage metrics, and minimizes administrative overhead by handling VM creation, load balancing, and scaling policies as a single resource. This aligns with the requirement for 20 identical VMs with automatic scale-out based on CPU usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
20 individual virtual machines in the same resource group.
Why it's wrong here
Twenty standalone VMs in one resource group share no scale set, so no autoscale rules can add or remove instances, and each VM is patched and configured separately. This suits small, individually managed workloads. Identical autoscaling web tiers require a virtual machine scale set.
When this WOULD be correct
This option would be correct if the question required deploying a small number of VMs with different configurations or applications, and autoscaling was not needed. For example: 'You need to deploy two VMs running different operating systems for testing purposes.'
- ✓
A Virtual Machine Scale Set.
Why this is correct
A Virtual Machine Scale Set provisions identical instances from a shared image and scales them automatically on metrics such as CPU usage, meeting the scale-out requirement while minimising administrative overhead through centralised configuration and management.
- ✗
An availability set.
Why it's wrong here
An availability set only distributes VMs across fault and update domains for resilience; it has no autoscale engine and no template-driven provisioning. It suits a fixed number of VMs needing high availability. Automatic CPU-based scale-out requires a virtual machine scale set instead.
- ✗
Azure Container Instances.
Why it's wrong here
Container Instances run single containers without a VM scale set, load balancer or autoscale rules, so CPU-based scale-out across 20 hosts is unavailable. It suits short-lived, bursty or event-driven containers. Virtual machine scale sets provide the autoscaling and identical VM deployment this scenario requires.
When this WOULD be correct
You need to deploy a microservice application in containers with fast startup times and minimal management overhead, and the scaling requirements are handled externally (e.g., by Azure Container Apps or a manual trigger). The question would specify 'containerized application' and 'no need for automatic VM-level scaling'.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AZ-104 exam frequently reuses these exact scenarios with slightly different constraints.
✓A Virtual Machine Scale Set.Correct answer▾
Why this is correct
A Virtual Machine Scale Set provisions identical instances from a shared image and scales them automatically on metrics such as CPU usage, meeting the scale-out requirement while minimising administrative overhead through centralised configuration and management.
✗20 individual virtual machines in the same resource group.Wrong answer — click to see why▾
Why this is wrong here
Managing 20 individual VMs requires manual configuration and lacks built-in autoscaling; it increases administrative overhead and does not meet the requirement for automatic scale-out based on CPU usage.
★ When this WOULD be the correct answer
This option would be correct if the question required deploying a small number of VMs with different configurations or applications, and autoscaling was not needed. For example: 'You need to deploy two VMs running different operating systems for testing purposes.'
Why candidates choose this
Candidates may think deploying individual VMs is simpler or more familiar, and they might overlook the autoscaling requirement or underestimate the administrative overhead of managing many VMs separately.
✗Azure Container Instances.Wrong answer — click to see why▾
Why this is wrong here
Azure Container Instances are designed for running containers without managing VMs, but they do not support automatic scale-out based on CPU usage natively; scaling requires additional orchestration like Azure Container Apps or Kubernetes, increasing administrative overhead.
★ When this WOULD be the correct answer
You need to deploy a microservice application in containers with fast startup times and minimal management overhead, and the scaling requirements are handled externally (e.g., by Azure Container Apps or a manual trigger). The question would specify 'containerized application' and 'no need for automatic VM-level scaling'.
Why candidates choose this
Candidates may think containers are always simpler and more scalable, overlooking that Azure Container Instances lack built-in autoscaling and are not ideal for identical VM workloads requiring CPU-based scale-out.
Analysis generated from the official AZ-104blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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About these practice questions
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Same concept, more angles
1 more way this is tested on AZ-104
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. A stateless API runs on Azure VMs and experiences unpredictable traffic spikes during the day. The administrator must automatically add or remove identical VM instances based on CPU usage, and the platform should distribute instances across fault domains without manual placement. What should be used?
medium- A.Availability set
- ✓ B.Virtual machine scale set
- C.Availability zone
- D.Proximity placement group
Why B: Virtual machine scale sets (VMSS) automatically manage identical VM instances and can scale out/in based on CPU usage metrics via autoscale rules. They distribute instances across fault domains automatically without manual placement, ensuring high availability during unpredictable traffic spikes.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-104 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-104 exam.