CKAD Application Deployment Practice Question
Which THREE components are essential for setting up Horizontal Pod Autoscaling (HPA) based on CPU utilization? (Select three)
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
✓
An HPA resource targeting the Deployment
Option C is correct because the HPA controller scales a target workload, and in this scenario the HPA resource must be created with a scaleTargetRef pointing to the Deployment so it can adjust the replica count. Option D is correct because CPU utilization-based HPA relies on the metrics.k8s.io API provided by metrics-server; without metrics-server installed and registered, the HPA cannot retrieve pod CPU usage and will report unknown metrics. Option E is correct because HPA calculates CPU utilization as a percentage of the container's CPU request, so each container in the target pods must have resources.requests.cpu set for the utilization target to be computed. Option A is not required because readiness probes affect traffic routing and pod availability, not the HPA's ability to read CPU metrics. Option B is not required because a LoadBalancer Service only exposes the workload externally and has no role in HPA metric collection or scaling decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A readiness probe on the pod
Why it's wrong here
A readiness probe governs whether a pod receives traffic; it does not supply the CPU utilisation signal HPA compares against target thresholds. Readiness probes would be the correct component when gating Service endpoints, whereas autoscaling depends on metrics-server and declared resource requests.
- ✗
A Service of type LoadBalancer
Why it's wrong here
A LoadBalancer Service exposes pods externally and plays no part in the metrics pipeline HPA consumes. It would be the right component when publishing a workload to external clients, whereas CPU-based autoscaling requires metrics-server plus resource requests on the container spec.
- ✓
An HPA resource targeting the Deployment
Why this is correct
The HPA controller reconciles a HorizontalPodAutoscaler object against a scalable target, so the HPA resource must reference the Deployment as its scaleTargetRef. Without it, no autoscaling logic runs, regardless of metrics availability or container resource requests.
- ✓
metrics-server installed in the cluster
Why this is correct
CPU utilisation is computed as a percentage of requested CPU, and that metric is served by the metrics-server aggregating kubelet cAdvisor data through the metrics.k8s.io API. Without metrics-server, the HPA cannot retrieve current utilisation and reports unknown metrics.
- ✓
CPU resource requests set on the container
Why this is correct
The HPA calculates utilisation as actual CPU usage divided by the container's CPU request, so every targeted container needs a CPU request defined. Missing requests leave utilisation undefined, and the HPA cannot compute a scaling decision.
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