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Workloads and SchedulinghardMultiple SelectObjective-mapped

CKA Workloads and Scheduling Practice Question

Which THREE of the following are true about HorizontalPodAutoscaler (HPA)?

⚠ Common exam trap

Test-takers frequently assume HPA only supports CPU metrics, but it also supports memory and custom metrics, and they confuse horizontal scaling (replicas) with vertical scaling (in-place resizing), which is not supported by HPA.

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

HPA can use custom metrics from the Kubernetes Metrics Server.

The HorizontalPodAutoscaler (HPA) can use custom metrics provided by the Kubernetes Metrics Server, such as requests per second or queue length, in addition to standard CPU and memory metrics. The HPA retrieves these metrics via the `metrics.k8s.io` API (for resource metrics) or custom metrics APIs, enabling scaling based on application-specific behavior.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • HPA can use custom metrics from the Kubernetes Metrics Server.

    Why this is correct

    HPA can use custom metrics via the custom.metrics.k8s.io API.

  • HPA supports in-place pod resizing.

    Why it's wrong here

    HPA scales replicas, not pod resources.

  • HPA cannot scale based on memory utilization.

    Why it's wrong here

    HPA can scale based on memory utilization.

  • HPA can be configured with target average CPU utilization.

    Why this is correct

    This is a common HPA configuration.

  • HPA can scale Deployments and StatefulSets.

    Why this is correct

    HPA works with both.

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