Quick Answer
The answer is the HorizontalPodAutoscaler (HPA), which is the correct Kubernetes resource to configure for automatically scaling pod replicas based on HTTP request load in Azure Kubernetes Service (AKS). HPA works by continuously monitoring custom metrics—such as HTTP request rate—along with standard CPU or memory usage, and then adjusting the replica count of a Deployment or ReplicaSet to match a defined target. On the Microsoft Azure Developer Associate AZ-204 exam, this concept tests your understanding of scaling strategies within AKS, often appearing in scenario-based questions where you must distinguish between HPA, Cluster Autoscaler, and manual scaling. A common trap is confusing HPA with the Cluster Autoscaler, but remember: HPA scales pods within a cluster, while Cluster Autoscaler scales the node pool itself. For a quick memory tip, think “HPA handles the pods, CA handles the nodes.”
This AZ-204 practice question tests your understanding of develop azure compute solutions. 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.
Your team develops a containerized web app using Azure Kubernetes Service (AKS). You need to ensure that the application can automatically scale based on HTTP request load. Which Kubernetes resource should you configure?
VerticalPodAutoscaler
Why wrong: Adjusts resource requests/limits, not scaling on load.
PodDisruptionBudget
Why wrong: Ensures minimum available pods during disruptions.
HorizontalPodAutoscaler
Correctly scales based on load metrics.
NetworkPolicy
Why wrong: Controls pod-to-pod communication.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
HorizontalPodAutoscaler
The HorizontalPodAutoscaler (HPA) is the correct Kubernetes resource for automatically scaling the number of pod replicas based on observed CPU, memory, or custom metrics like HTTP request rate. In an AKS cluster, HPA adjusts the replica count of a Deployment or ReplicaSet to match the target metric, enabling the application to handle varying HTTP load without manual intervention.
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.
VerticalPodAutoscaler
Why it's wrong here
Adjusts resource requests/limits, not scaling on load.
PodDisruptionBudget
Why it's wrong here
Ensures minimum available pods during disruptions.
HorizontalPodAutoscaler
Why this is correct
Correctly scales based on load metrics.
Related concept
Read the scenario before looking for a memorised answer.
NetworkPolicy
Why it's wrong here
Controls pod-to-pod communication.
Common exam traps
The trap here is that candidates often confuse HorizontalPodAutoscaler with VerticalPodAutoscaler, mistakenly thinking that adjusting pod resources (CPU/memory) is the correct way to handle HTTP load, when in fact HPA scales the number of pod replicas horizontally to distribute the load.
Detailed technical explanation
HPA works by querying the Kubernetes Metrics Server (or a custom metrics adapter like Prometheus) at a default interval of 15 seconds, comparing the current metric value (e.g., average CPU utilization across pods) against a target threshold, and calculating the desired replica count using the formula `desiredReplicas = ceil[currentReplicas * (currentMetricValue / targetMetricValue)]`. For HTTP-based scaling, you would configure a custom metric from an ingress controller (e.g., NGINX Ingress) exposing requests-per-second, or use the Kubernetes Event-Driven Autoscaler (KEDA) for more advanced event-driven scaling.
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: HorizontalPodAutoscaler — The HorizontalPodAutoscaler (HPA) is the correct Kubernetes resource for automatically scaling the number of pod replicas based on observed CPU, memory, or custom metrics like HTTP request rate. In an AKS cluster, HPA adjusts the replica count of a Deployment or ReplicaSet to match the target metric, enabling the application to handle varying HTTP load without manual intervention.
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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