- A
Migrate all workflows to GitHub-hosted runners.
Why wrong: This may not be feasible due to cost or compliance.
- B
Reduce the number of concurrent jobs in each workflow.
Why wrong: Reducing concurrency may increase wait times.
- C
Implement auto-scaling for self-hosted runners using a scale set or Kubernetes-based runner controller.
Auto-scaling adds runners during peak demand and removes when idle.
- D
Increase the size of the existing self-hosted runner VMs to handle more jobs.
Why wrong: Larger VMs can run more jobs but still limited by concurrency.
Question 518 of 928
How to Auto-Scale Self-Hosted GitHub Actions Runners
This AZ-400 practice question tests your understanding of design and implement build and release pipelines. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Your organization uses GitHub Actions with self-hosted runners on Azure virtual machines. You notice that some workflows are taking longer than expected because runners are busy. You need to improve the performance without adding more permanent runners. Which solution should you implement?
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
✓
Implement auto-scaling for self-hosted runners using a scale set or Kubernetes-based runner controller.
Option C is correct because auto-scaling self-hosted runners using a scale set or a Kubernetes-based runner controller (e.g., actions-runner-controller) dynamically provisions and deprovisions runner instances based on workflow demand. This eliminates idle runner waste while ensuring sufficient capacity during peak loads, directly addressing the bottleneck without adding permanent infrastructure.
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.
- ✗
Migrate all workflows to GitHub-hosted runners.
Why it's wrong here
This may not be feasible due to cost or compliance.
- ✗
Reduce the number of concurrent jobs in each workflow.
Why it's wrong here
Reducing concurrency may increase wait times.
- ✓
Implement auto-scaling for self-hosted runners using a scale set or Kubernetes-based runner controller.
Why this is correct
Auto-scaling adds runners during peak demand and removes when idle.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the size of the existing self-hosted runner VMs to handle more jobs.
Why it's wrong here
Larger VMs can run more jobs but still limited by concurrency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse vertical scaling (increasing VM size) with horizontal scaling (adding more runner instances), mistakenly believing a larger VM can process multiple jobs concurrently when in fact each self-hosted runner handles only one job at a time.
Detailed technical explanation
How to think about this question
Auto-scaling for self-hosted runners typically uses Azure VM Scale Sets or the actions-runner-controller on Kubernetes, which listens to GitHub's runner API for pending job requests. When the queue depth exceeds a threshold, new runner instances are created via ARM templates or Kubernetes pods, and they automatically register with the repository or organization. This approach ensures cost efficiency by scaling to zero when idle and scaling out rapidly during bursts, with a typical provisioning time of 2–5 minutes for VMs or seconds for containers.
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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Design and implement build and release pipelines — study guide chapter
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FAQ
Questions learners often ask
What does this AZ-400 question test?
Design and implement build and release pipelines — This question tests Design and implement build and release pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement auto-scaling for self-hosted runners using a scale set or Kubernetes-based runner controller. — Option C is correct because auto-scaling self-hosted runners using a scale set or a Kubernetes-based runner controller (e.g., actions-runner-controller) dynamically provisions and deprovisions runner instances based on workflow demand. This eliminates idle runner waste while ensuring sufficient capacity during peak loads, directly addressing the bottleneck without adding permanent infrastructure.
What should I do if I get this AZ-400 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.
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Last reviewed: Jul 4, 2026
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