CKA Workloads and Scheduling Practice Question
You have a HorizontalPodAutoscaler targeting a Deployment with minReplicas=2 and maxReplicas=10. currentReplicas is 2. The HPA uses average CPU utilization across pods, targetting 80% of the requested CPU. Pod CPU request is 500m. The current average CPU utilization is 90%. What will the HPA do?
⚠ Common exam trap
The trap here is that candidates forget the HPA uses ceiling (ceil) in its calculation and incorrectly round up to 4, or assume a stabilization delay applies to scale-up, leading them to choose 'do nothing'.
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 to 3 replicas
The HPA calculates the desired number of replicas as ceil(currentReplicas * (currentUtilization / targetUtilization)). Here, currentUtilization is 90% and targetUtilization is 80%, so desiredReplicas = ceil(2 * (90/80)) = ceil(2.25) = 3. Since 3 is within the min/max range (2–10) and differs from currentReplicas (2), the HPA will scale up to 3 replicas immediately (no stabilization window for scale-up by default).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Do nothing; wait for stabilization
Why it's wrong here
The Horizontal Pod Autoscaler calculates replica requirements immediately upon detecting a metric deviation outside the tolerance band (typically 10% by default). Since the ratio of current to target metric (90/80 = 1.125) exceeds this tolerance, the HPA will initiate a scaling action immediately rather than waiting for a stabilization window, which is primarily used to delay scale-down actions to prevent thrashing.
- ✓
Scale up to 3 replicas
Why this is correct
The HPA uses the formula desiredReplicas = ceil[currentReplicas * (currentMetricValue / targetMetricValue)]. Plugging in the values yields ceil[2 * (90 / 80)] = ceil[2.25], which rounds up to 3 replicas. This scaling action directly addresses the resource deficit by distributing the load across an additional pod to bring average utilization back down toward the 80% target.
- ✗
Scale up to 4 replicas
Why it's wrong here
Scaling to 4 replicas overestimates the required capacity and represents an incorrect mathematical calculation. The ceiling function in the HPA algorithm rounds the fractional result of 2.25 up to the next whole integer, which is 3, not 4. Overscaling by adding two replicas unnecessarily consumes cluster resources and violates the precise algorithmic behavior of the Kubernetes control loop.
- ✗
Scale down to 1 replica because utilization is too high
Why it's wrong here
Reducing the replica count to 1 is the opposite of the required behavior when resource utilization exceeds the target threshold. When current utilization (90%) is higher than the target (80%), the workload is under-provisioned, necessitating a scale-up event. Scaling down to a single replica would severely exacerbate the resource constraint, likely leading to application degradation or Out-Of-Memory (OOM) kills.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CKA practice question is part of Courseiva's free CNCF certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CKA exam.