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
CPU insufficiency does not cause OOMKilled; it would manifest as slow performance or CPU throttling.
- ✗
The pod is configured with wrong GPU drivers; update drivers.
Why it's wrong here
GPU driver issues typically cause different errors, not OOMKilled.
- ✗
The model is too large; use a smaller model.
Why it's wrong here
While a large model could cause high memory usage, the exhibit shows OOMKilled due to limit, not necessarily model size.
- ✓
The container memory limit is too low; increase the memory limit in the pod spec.
Why this is correct
OOMKilled specifically indicates memory exhaustion; raising the limit is the direct fix.
Visual reference
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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