The answer is an Azure Monitor autoscale rule configured directly on the virtual machine scale set. This is correct because autoscale rules use metrics like average CPU utilization to trigger scale-out actions when a threshold—such as 75%—is exceeded, and scale-in actions when utilization drops below a lower threshold like 30%. On the AZ-104 exam, this scenario tests your understanding of scaling VMSS instances based on performance metrics, often appearing as a scenario where you must choose between autoscale rules, manual scaling, or scheduled scaling. A common trap is confusing autoscale rules with Azure Load Balancer health probes or availability set configurations, which do not handle metric-based instance count adjustments. Remember the key: autoscale rules are metric-driven and applied at the VMSS resource level, not at the individual VM level. Memory tip: think "CPU up at 75, down at 30" and associate the rule with the scale set’s "Scaling" blade in the portal.
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.
You have a virtual machine scale set that must increase the number of instances automatically when average CPU utilization exceeds 75 percent and decrease when utilization drops below 30 percent. What should you configure?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
An Azure Monitor autoscale rule on the scale set
Azure Monitor autoscale rules allow you to define conditions for automatically scaling out (increasing instances) when average CPU utilization exceeds a threshold (e.g., 75%) and scaling in (decreasing instances) when it drops below a lower threshold (e.g., 30%). These rules are applied directly to the virtual machine scale set, enabling dynamic scaling based on performance metrics.
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.
✓
An Azure Monitor autoscale rule on the scale set
Why this is correct
Autoscale rules support scaling out and in based on CPU thresholds.
Related concept
Read the scenario before looking for a memorised answer.
✗
A boot diagnostics configuration
Why it's wrong here
Boot diagnostics captures startup logs and screenshots, not scaling logic.
✗
An availability set
Why it's wrong here
Availability sets improve resilience but do not provide autoscaling.
✗
A custom script extension
Why it's wrong here
A script extension can run scripts on VMs, but it is not the native scaling mechanism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse autoscale rules with other VM configuration options like boot diagnostics or custom script extensions, not realizing that autoscaling is a dedicated feature of Azure Monitor applied to scale sets.
Detailed technical explanation
How to think about this question
Autoscale rules in Azure Monitor use a default cool-down period of 5 minutes (configurable) to prevent flapping, and they evaluate metrics every 1 minute. The scale-out and scale-in thresholds are typically set with a buffer (e.g., scale-out at 75%, scale-in at 30%) to avoid rapid oscillations. In a real-world scenario, you might also configure a minimum and maximum instance count to control costs and ensure availability.
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: An Azure Monitor autoscale rule on the scale set — Azure Monitor autoscale rules allow you to define conditions for automatically scaling out (increasing instances) when average CPU utilization exceeds a threshold (e.g., 75%) and scaling in (decreasing instances) when it drops below a lower threshold (e.g., 30%). These rules are applied directly to the virtual machine scale set, enabling dynamic scaling based on performance metrics.
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
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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. The operations team wants 3 to 8 identical Linux VM instances, with more instances added when average CPU stays above 70 percent for 10 minutes and removed when load falls. Which three settings should be configured? Select three.
medium
✓ A.Use a virtual machine scale set for the identical application instances.
✓ B.Create an autoscale profile with a scale-out rule based on average CPU utilization.
✓ C.Set minimum and maximum instance counts so the service cannot scale below 3 or above 8.
D.Place the VMs in an availability set instead of using a scale set.
E.Clone the VM manually whenever CPU rises and remove clones by hand later.
Why A: A virtual machine scale set (VMSS) is the correct Azure service for deploying and managing a group of identical, load-balanced Linux VMs that can automatically scale in and out based on demand. It supports autoscaling rules that adjust the instance count within a defined range, meeting the requirement for 3 to 8 identical instances with automatic addition when average CPU exceeds 70% for 10 minutes and removal when load falls.
Variation 2. A stateless Linux API should start with 2 instances, scale out to 6 when average CPU stays above 75 percent for 10 minutes, and scale back in when load drops. Which Azure compute resource should the administrator deploy?
medium
A.An availability set with manual VM resizing.
✓ B.A virtual machine scale set with autoscale rules.
C.A single Standard D-series VM with scheduled shutdown.
D.A load balancer in front of two unmanaged VMs.
Why B: A virtual machine scale set (VMSS) with autoscale rules is the correct choice because it natively supports scaling out and scaling in based on performance metrics like average CPU percentage. The requirement for a stateless Linux API with a minimum of 2 instances, scaling to 6 when CPU exceeds 75% for 10 minutes, and scaling back in when load drops is exactly the use case VMSS is designed for. Autoscale rules can be configured to use a scale-out and scale-in policy with a cool-down period, ensuring the application remains responsive while optimizing cost.
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Question Discussion
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