CKAD Application Deployment Practice Question
A HorizontalPodAutoscaler (HPA) is configured to scale a Deployment based on CPU utilization. The target CPU utilization percentage is set to 80%. The current CPU utilization is 90%. What will the HPA do?
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
✓
Scale up the number of replicas
The HPA will increase the number of replicas to bring CPU utilization down towards 80%. The exact new replicas depends on the metric, but generally it scales up.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the pod with the highest CPU usage
Why it's wrong here
The HorizontalPodAutoscaler does not manage or delete specific pods; it reconciles the Deployment's replica count based on observed metrics. Manually deleting a pod would cause Kubernetes to create a replacement via the ReplicaSet, and if that pod is the one consuming the most CPU, the new pod may inherit the same utilization pattern, leaving the average CPU across the fleet unchanged or even worsened. This action also risks downtime for in-flight requests and contradicts the HPA's declarative scaling model.
- ✓
Scale up the number of replicas
Why this is correct
The HPA calculates the desired replicas using the formula desiredReplicas = ceil(currentReplicas * (currentMetric / targetMetric)). With current average CPU at 90% and target at 80%, the ratio is 1.125, so the HPA will increase the replica count to spread traffic and reduce per-pod CPU utilization back toward 80%. This is the only response aligned with the HPA's design, as scaling out dilutes workload across more pods and brings the metric within the target threshold.
- ✗
Do nothing because 90% is within acceptable range
Why it's wrong here
An HPA's target is a utilization threshold, not a tolerance band; any sustained value above the target triggers a scale-out operation. At 90%, the current metric exceeds the 80% target by 10 percentage points, so the control loop will compute a desired replica count greater than the current count. Doing nothing would leave the deployment over-utilized and violate the HPA's desired state, potentially leading to degraded latency or pod throttling.
- ✗
Scale down the number of replicas
Why it's wrong here
Scaling down reduces the number of pod replicas, which concentrates the same incoming workload onto fewer instances and drives per-pod CPU utilization even higher. For example, if 5 pods average 90% CPU, scaling to 4 would push the average toward 112.5%, exceeding not only the target but likely exhausting the CPU requests/limits. This action moves the deployment in the opposite direction of the HPA's corrective intent and can cause cascading failures from resource saturation.
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Written by Johnson Ajibi, MSc IT Security
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