AI0-001 AI Implementation and Operations Practice Question
Exhibit
Refer to the exhibit. $ kubectl get pods NAME READY STATUS RESTARTS AGE ml-service-7b9c8f-2k4d 0/1 OOMKilled 3 5m ml-service-7b9c8f-j5p1 1/1 Running 0 10m $ kubectl logs ml-service-7b9c8f-2k4d 2025/03/15 14:23:45 [FATAL] Out of memory: Killed process 1234 (python)
Based on the exhibit, what is the most likely cause of the pod failure and its solution?
⚠ Common exam trap
CompTIA often tests the distinction between resource exhaustion errors (OOMKilled vs. CPU throttling) and configuration errors (driver issues), leading candidates to incorrectly attribute a memory limit issue to a hardware or driver problem.
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 container memory limit is too low; increase the memory limit in the pod spec.
The pod failure is caused by an OOMKilled (Out of Memory) error, as indicated by the pod status in the exhibit. When a container exceeds its memory limit, Kubernetes terminates it with an OOMKilled exit code. Increasing the memory limit in the pod spec allows the container to allocate more memory, resolving the failure.
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 node has insufficient CPU; add more CPU.
Why it's wrong here
Insufficient CPU produces pending or throttled pods, not the crash or image-pull failure shown in the exhibit. It is tempting because CPU requests are a common scheduling cause, and would be correct if the pod events showed FailedScheduling with insufficient cpu on the node.
- ✗
The pod is configured with wrong GPU drivers; update drivers.
Why it's wrong here
Wrong GPU drivers cause runtime errors only when the container actually requests a GPU; the exhibit shows a different failure signature. It is tempting because driver mismatches genuinely break GPU workloads, and would be correct if logs showed CUDA initialisation errors after a successful image pull.
- ✗
The model is too large; use a smaller model.
Why it's wrong here
Model size affects memory and load time, not the failure state shown in the exhibit. It is tempting because oversized models do cause OOMKilled pods, and would be correct if the exhibit showed memory-limit exhaustion during model loading rather than the actual reported condition.
- ✓
The container memory limit is too low; increase the memory limit in the pod spec.
Why this is correct
The container exceeded its configured memory limit, so the kernel terminated it with an OOMKilled status. Raising the memory limit in the pod spec allows the workload to complete, satisfying the resource constraint the exhibit shows the container breaching.
Visual reference
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.