CKA Troubleshooting Practice Question
You run 'kubectl get events --sort-by=.lastTimestamp' and see the following events for a pod: 'Warning FailedScheduling 0/3 nodes are available: 3 Insufficient cpu'. What is the most likely solution?
⚠ Common exam trap
Many exam-takers confuse CPU requests with CPU limits and mistakenly think increasing limits will help the pod get scheduled, but the scheduler only evaluates requests, not limits.
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
✓
Reduce the CPU request for the pod or remove other workloads to free CPU
The event '0/3 nodes are available: 3 Insufficient cpu' indicates that all three nodes in the cluster lack sufficient allocatable CPU to satisfy the pod's CPU request. The most direct solution is to either reduce the pod's CPU request (so it fits on an existing node) or remove other workloads to free up CPU capacity. This aligns with Kubernetes resource scheduling, where the scheduler only considers requests (not limits) when placing pods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the CPU request for the pod or remove other workloads to free CPU
Why this is correct
The Kubernetes scheduler uses a pod's CPU request to determine node feasibility during the filtering phase. Lowering this request value reduces the resource footprint required for scheduling, allowing the pod to fit onto existing nodes with limited allocatable CPU. Alternatively, evicting or deleting non-essential workloads frees up allocatable capacity on those nodes, resolving the scheduling bottleneck without requiring infrastructure changes.
- ✗
Change the scheduler to a different one
Why it's wrong here
Replacing the default kube-scheduler with an alternative scheduler will not resolve this issue because the bottleneck is physical resource exhaustion, not scheduling logic. Any standard Kubernetes scheduler must respect the node's allocatable capacity limits to prevent overcommit and node instability. Consequently, a different scheduler would still fail to bind the pod to any node lacking sufficient unreserved CPU.
- ✗
Increase the CPU limit for the pod
Why it's wrong here
Modifying the CPU limit has no direct impact on the scheduler's initial placement decisions, which are strictly calculated based on the pod's CPU request. If the CPU request is omitted or automatically scaled up alongside the limit, increasing the limit would actually worsen the scheduling failure by demanding even more unreserved CPU from the nodes. This action fails to address the lack of available allocatable capacity in the cluster.
- ✗
Add more nodes to the cluster
Why it's wrong here
While provisioning additional worker nodes would scale cluster capacity and eventually allow the pod to schedule, it is an expensive and heavy-handed infrastructure change. In a CKA context, optimizing the workload's resource requests or managing existing cluster tenant distribution should always be prioritized before scaling physical or virtual infrastructure. This option represents an inefficient workaround rather than a direct resolution of the workload configuration issue.
Go deeper
Related to this question
Learn chapter
Kubernetes Architecture Overview
Key term
Network Policies
A Kubernetes resource that controls how pods communicate with each other and with other network endpoints, acting as a firewall for pod-to-pod traffic.
Key term
kubectl Command Reference
kubectl is the command-line tool used to interact with and manage Kubernetes clusters by sending commands to the Kubernetes API.
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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.