Quick Answer
The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically adjusts the number of pod replicas in an AKS deployment based on observed metrics, including custom metrics like queue depth. HPA works by querying the Kubernetes Metrics API, and for custom metrics, you extend this API using a custom metrics adapter—such as the Prometheus Adapter—which exposes application-specific data like message queue length or request latency. On the AZ-204 exam, this scenario tests your understanding of scaling beyond CPU and memory; a common trap is confusing HPA with the Cluster Autoscaler, which scales the node pool rather than individual pods. Remember that HPA handles pod-level scaling based on any metric you define, while the Cluster Autoscaler adds or removes nodes. For the exam, think of HPA as the “microservice scaler” and the Cluster Autoscaler as the “infrastructure scaler.” A helpful memory tip: HPA = Horizontal Pod Autoscaler = “How Pods Adapt” to custom metrics.
This AZ-204 practice question tests your understanding of develop azure compute solutions. 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.
A company deploys a microservices application on Azure Kubernetes Service (AKS). They need to automatically scale individual microservices based on custom metrics (e.g., queue depth). Which feature should they use?
Horizontal Pod Autoscaler
HPA can scale pods based on custom metrics from Azure Monitor or Prometheus.
Virtual Node
Why wrong: Virtual Node scales the cluster's node pool, not individual pods.
Vertical Pod Autoscaler
Why wrong: VPA adjusts CPU/memory requests, not scaling based on custom metrics.
Cluster Autoscaler
Why wrong: Cluster Autoscaler adjusts the number of nodes, not pods.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Horizontal Pod Autoscaler
The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically scales the number of pod replicas in a deployment or replica set based on observed metrics, including custom metrics like queue depth. HPA queries the Kubernetes Metrics API, which can be extended with custom metrics adapters (e.g., Prometheus Adapter) to support application-specific metrics, enabling fine-grained scaling for each microservice.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Horizontal Pod Autoscaler
Why this is correct
HPA can scale pods based on custom metrics from Azure Monitor or Prometheus.
Related concept
Read the scenario before looking for a memorised answer.
Virtual Node
Why it's wrong here
Virtual Node scales the cluster's node pool, not individual pods.
Vertical Pod Autoscaler
Why it's wrong here
VPA adjusts CPU/memory requests, not scaling based on custom metrics.
Cluster Autoscaler
Why it's wrong here
Cluster Autoscaler adjusts the number of nodes, not pods.
Common exam traps
The trap here is that candidates often confuse Horizontal Pod Autoscaler (scaling replicas) with Cluster Autoscaler (scaling nodes) or Vertical Pod Autoscaler (scaling pod resources), but only HPA supports custom metrics for per-microservice replica scaling.
Detailed technical explanation
Under the hood, HPA works by periodically (default every 15 seconds) fetching metric values from the Metrics Server or a custom metrics API, then calculating the desired replica count using the formula: desiredReplicas = ceil[currentReplicas * (currentMetricValue / desiredMetricValue)]. For custom metrics, you must deploy a custom metrics adapter (e.g., Prometheus Adapter or Azure Monitor Adapter) that exposes the metric via the `custom.metrics.k8s.io` API. A real-world scenario is scaling a worker microservice based on Azure Storage Queue depth, where the adapter reads queue length and HPA adjusts replicas to process messages efficiently.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Develop Azure compute solutions — This question tests Develop Azure compute solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Horizontal Pod Autoscaler — The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically scales the number of pod replicas in a deployment or replica set based on observed metrics, including custom metrics like queue depth. HPA queries the Kubernetes Metrics API, which can be extended with custom metrics adapters (e.g., Prometheus Adapter) to support application-specific metrics, enabling fine-grained scaling for each microservice.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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