The answer is a virtual machine scale set with autoscale enabled, because it is the only Azure compute service that natively supports running multiple identical Windows VMs while automatically scaling out when the average CPU stays above 70% for 10 minutes. This VMSS autoscale based on CPU threshold for stateless API workload is achieved by configuring a scale-out rule that triggers when the average CPU percentage metric exceeds the 70% threshold over a 10-minute gathering period, and the platform automatically distributes new instances across fault domains and availability zones to improve resiliency. On the AZ-104 exam, this question tests your ability to match the right compute service to specific scaling and resiliency requirements, with a common trap being to choose Azure App Service or a load balancer with separate VMs—neither of which supports identical VM instances with autoscale rules defined in Bicep. A useful memory tip: think of VMSS as the "stateless swarm" service—it handles identical, disposable VMs that scale in and out based on a single CPU metric, just like a swarm of bees responding to hive activity.
AZ-104 Deploy and Manage Azure Compute Practice Question
This AZ-104 practice question tests your understanding of deploy and manage azure compute. 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.
A stateless API must run 4 to 12 identical Windows VMs, scale out automatically when average CPU stays above 70% for 10 minutes, and distribute instances to improve resiliency. Deployments are defined in Bicep. Which Azure compute service should be used?
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 with autoscale enabled
A virtual machine scale set (VMSS) with autoscale enabled is the correct choice because it supports running multiple identical Windows VMs, can automatically scale out when average CPU exceeds 70% for 10 minutes, and distributes instances across fault domains and availability zones for resiliency. Bicep deployments natively define VMSS configurations, making it the ideal compute service for stateless, scalable API workloads.
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.
✗
A single Azure VM with an autoscale setting
Why it's wrong here
A lone VM cannot scale horizontally into multiple identical instances by itself.
✓
A virtual machine scale set with autoscale enabled
Why this is correct
VM scale sets are designed for many identical instances and work naturally with autoscale profiles.
Related concept
Read the scenario before looking for a memorised answer.
✗
An availability set with manual VM resizing
Why it's wrong here
Availability sets improve resilience but do not provide automatic horizontal scaling.
✗
Azure Container Instances running one container per request
Why it's wrong here
Container instances do not match the requirement for Windows VM-based deployments and scale-set management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse autoscale settings (which only apply to scale sets or PaaS services) with individual VMs, or assume an availability set provides autoscaling, when in fact it only ensures fault tolerance for a static number of VMs.
Detailed technical explanation
How to think about this question
Under the hood, VMSS uses an autoscale profile with a scale-out rule that triggers when the 'Percentage CPU' metric exceeds 70% for a 10-minute aggregation window (using the 'Average' statistic and a 10-minute 'TimeGrain'). The scale set distributes VMs across fault domains (up to 3) and, if configured, availability zones to meet a 99.95% SLA. In real-world scenarios, you must also configure a scale-in rule to avoid thrashing, and use a 'cool-down' period (e.g., 5 minutes) between scaling events to stabilize the metric.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-104 question in full detail.
Deploy and Manage Azure Compute — This question tests Deploy and Manage Azure Compute — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A virtual machine scale set with autoscale enabled — A virtual machine scale set (VMSS) with autoscale enabled is the correct choice because it supports running multiple identical Windows VMs, can automatically scale out when average CPU exceeds 70% for 10 minutes, and distributes instances across fault domains and availability zones for resiliency. Bicep deployments natively define VMSS configurations, making it the ideal compute service for stateless, scalable API workloads.
What should I do if I get this AZ-104 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 →
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 need to deploy a set of identical Azure virtual machines that can automatically scale out during demand spikes and scale in when usage falls. Which Azure feature should you use?
medium
A.An availability set
✓ B.A Virtual Machine Scale Set
C.A Recovery Services vault
D.Boot diagnostics
Why B: A Virtual Machine Scale Set (VMSS) is the correct Azure feature because it is designed specifically to deploy and manage a set of identical, load-balanced VMs that can automatically scale out (increase instance count) during demand spikes and scale in (decrease instance count) when usage falls, using autoscale rules based on metrics like CPU or memory.
Variation 2. You need to run a stateless web workload on Azure virtual machines and automatically increase or decrease instance count based on demand. You also want a single management boundary for the VM instances. Which solution should you deploy?
hard
✓ A.A Virtual Machine Scale Set
B.An availability set with individual VMs
C.Azure Backup
D.A Recovery Services vault
Why A: A Virtual Machine Scale Set (VMSS) is the correct solution because it provides an autoscaling group of identical VMs that can automatically increase or decrease instance count based on demand (e.g., CPU or memory metrics). It also offers a single management boundary, allowing you to manage, monitor, and scale all instances as a unified resource rather than individually.
Last reviewed: Jun 11, 2026
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