CKA Practice Question: Cluster Architecture, Installation and Configuration
A pod in the 'production' namespace is in a CrashLoopBackOff state. The pod has been running successfully for several days. You run 'kubectl describe pod app-pod -n production' and see the message: 'OOMKilled'. What is the MOST appropriate action to resolve this issue?
⚠ Common exam trap
It's easy for candidates to confuse OOMKilled with a CPU throttling issue or think that simply restarting the pod will fix the problem, when in fact the memory limit must be adjusted to prevent the OOM killer from terminating the container.
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
✓
Increase the memory limit in the pod's container resource specification
The pod is in CrashLoopBackOff with an OOMKilled message, which indicates the container was terminated because it exceeded its memory limit. The most appropriate action is to increase the memory limit in the pod's container resource specification, allowing the container to allocate more memory without being killed by the Out-Of-Memory (OOM) killer.
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 and recreate the pod to clear the crash loop
Why it's wrong here
Deleting and recreating the pod will only provide a temporary restart, as the underlying issue causing the CrashLoopBackOff persists. If the pod is crashing due to an OOMKilled event, simply restarting it will lead to the container exceeding its memory limit again and being terminated by the kernel. This action does not address the root cause, resulting in the pod quickly returning to a CrashLoopBackOff state.
- ✓
Increase the memory limit in the pod's container resource specification
Why this is correct
An OOMKilled event signifies that the container attempted to consume more memory than its configured limits.memory in the pod's resource specification. By increasing this memory limit, you provide the container with additional RAM, allowing it to operate without exceeding its allocated resources. This directly resolves the memory exhaustion issue, preventing the Linux kernel from terminating the process and clearing the CrashLoopBackOff.
- ✗
Increase the CPU request for the container
Why it's wrong here
Increasing the CPU request for a container addresses potential CPU throttling or contention, which can manifest as performance degradation or slowness, but not typically as an OOMKilled event. An OOMKilled status explicitly indicates memory exhaustion, meaning the container ran out of available RAM. Adjusting CPU resources will have no impact on a memory-related crash, leaving the pod in a CrashLoopBackOff state.
- ✗
Delete the namespace and redeploy all workloads
Why it's wrong here
Deleting an entire namespace is an extremely destructive and disproportionate action for resolving a single pod's CrashLoopBackOff issue. This would indiscriminately terminate all running workloads, services, and configurations within that namespace, causing significant downtime and data loss for all applications. It is an entirely inappropriate solution for a localized resource problem that can be fixed by adjusting a single pod's specification.
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Related to this question
Learn chapter
Managing Cluster Upgrades
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.
Key term
Pod Failure Troubleshooting
Pod failure troubleshooting is the process of identifying and resolving issues that cause Kubernetes pods to crash, restart, or become unavailable.
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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.