CKA Workloads and Scheduling Practice Question
A HorizontalPodAutoscaler (HPA) is configured for a Deployment with targetCPUUtilizationPercentage: 80. The current CPU utilization is 90%. The deployment has minReplicas: 3 and maxReplicas: 10. What will the HPA do?
⚠ Common exam trap
The CKA exam often tests the misconception that the HPA can directly manage cluster nodes, but the HPA only adjusts pod replicas; node autoscaling is a separate component.
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
✓
It increases the number of replicas.
The HPA increases the number of replicas because the current CPU utilization (90%) exceeds the target of 80%. The HPA calculates the desired replica count using the formula: desiredReplicas = ceil[currentReplicas * (currentMetricValue / targetMetricValue)], which yields ceil[3 * (90/80)] = ceil[3.375] = 4 replicas. This scales the deployment up to reduce per-pod CPU load, staying within the configured minReplicas (3) and maxReplicas (10).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It does nothing because the utilization is within the acceptable range.
Why it's wrong here
This statement is incorrect because the current average CPU utilization of 90% exceeds the configured target threshold of 80%. The Horizontal Pod Autoscaler (HPA) controller calculates the ratio of current metric value to target metric value, and since this ratio is greater than 1.0 (outside the default 10% tolerance band), it must take action to scale the deployment.
- ✗
It adds a new node to the cluster.
Why it's wrong here
This option confuses pod-level scaling with infrastructure-level scaling. The Horizontal Pod Autoscaler operates strictly on the Kubernetes workload level by modifying the replicas field of the target deployment or replica set. Adding physical or virtual worker nodes to the cluster is the responsibility of the Cluster Autoscaler, which triggers only when pods cannot be scheduled due to insufficient resource capacity.
- ✗
It decreases the number of replicas to reduce CPU usage.
Why it's wrong here
Reducing the replica count would concentrate the incoming traffic and workload onto fewer pods, which would further drive up individual CPU utilization and potentially cause application failure. To alleviate high CPU usage per pod, the HPA must distribute the load across a larger number of instances, thereby reducing the average resource consumption per pod back toward the target threshold.
- ✓
It increases the number of replicas.
Why this is correct
This is the correct behavior because the current average CPU utilization (90%) is higher than the target utilization (80%). The HPA controller uses the formula of desired replicas equals the ceiling of current replicas multiplied by the ratio of current metric value to target metric value. Since this ratio is greater than 1.0, the controller will increase the replica count of the deployment to distribute the load and bring utilization down.
About these practice questions
This CKA question is part of Courseiva's 726-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.