CKA Workloads and Scheduling Practice Question
Which TWO of the following are true about a HorizontalPodAutoscaler (HPA) using average CPU utilization? (Select TWO)
⚠ Common exam trap
Watch out — candidates often confuse CPU requests with CPU limits, assuming limits are used in HPA calculations, or they mistakenly think the HPA scales down when utilization is high.
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
✓
The HPA calculates utilization as the average CPU usage across all pods divided by the CPU request per pod
Option A is correct because the HPA computes average CPU utilization by taking the mean CPU usage across all targeted pods and dividing it by each pod's CPU request, expressing the result as a percentage of the request. Option B is correct because the utilization formula depends on the CPU request as its denominator; if a pod has no CPU request set, the HPA cannot compute a utilization percentage for it and will not act on that metric. Option C is wrong because the HPA target for CPU utilization is expressed as a percentage of the request (e.g., 50%), not an absolute CPU value. Option D is wrong because exceeding the target utilization triggers scale up, not scale down. Option E is wrong because the HPA uses CPU requests, not CPU limits, as the baseline for utilization calculations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The HPA calculates utilization as the average CPU usage across all pods divided by the CPU request per pod
Why this is correct
The HPA computes pod CPU utilization as the measured CPU usage for each pod divided by that pod's CPU request, then averages these per-pod utilization ratios across the entire set of selected pods. If every pod has the same CPU request, this equals the total usage across pods divided by a single request value, but the canonical definition is per-pod usage/request averaged.
- ✓
If a pod does not have a CPU request, the HPA cannot calculate its CPU utilization
Why this is correct
A CPU request supplies the denominator without which a utilization percentage cannot be derived. If a pod's containers have no CPU requests, the metrics pipeline has no reference threshold, so the HPA marks the metric as missing and refuses to autoscale on CPU for that pod. Requests are mandatory for percentage-based scaling decisions.
- ✗
The HPA target is an absolute CPU value, not a percentage
Why it's wrong here
The desired metric in an HPA object is expressed as a percentage of the CPU request, not as a fixed milli-core value like 200m. For example, targetAverageUtilization: 60 means each pod's observed CPU usage should be no more than 60% of its requested CPU. An absolute target would be meaningless across pods with different request sizes.
- ✗
If the average CPU utilization exceeds the target, the HPA will scale down
Why it's wrong here
When observed utilization crosses above the target percentage, the HPA increases the number of replicas to distribute load and bring average usage back down. Scaling down is triggered when current utilization is sufficiently below the target, not above it. Thus exceeding the target is the classic scale-up signal.
- ✗
The HPA uses CPU limits in its calculation of average utilization
Why it's wrong here
CPU limits only cap how much CPU a container may consume when under contention; they are not used in HPA math because a container can burst up to its limit without reflecting sustainable demand. The HPA deliberately bases scaling decisions on requests, which are guaranteed resources, to avoid reacting to throttled bursts. Therefore limits are irrelevant to utilization percentages.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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