mediumMultiple Choice
PMLE Practice Question: A model deployed on Vertex AI Prediction…
Exhibit
Refer to the exhibit.
```
Log entry:
{
"severity": "ERROR",
"message": "Model server process exited with code 137 (SIGKILL)",
"container": {
"memory_usage_mb": 4096,
"memory_limit_mb": 4096
},
"@type": "type.googleapis.com/google.cloud.ml.v1.PredictionLog"
}
```A model deployed on Vertex AI Prediction repeatedly exits with code 137. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between exit codes: candidates may confuse exit code 137 (OOM kill) with exit code 1 (generic error) or exit code 139 (segmentation fault), leading them to incorrectly attribute the issue to CPU or disk problems.
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 model is using more memory than allocated (4GB).
Exit code 137 indicates that the container was killed by the Linux kernel's Out-Of-Memory (OOM) killer. In Vertex AI Prediction, each model deployment has a fixed memory allocation (default 4GB for custom containers). When the model's inference process exceeds this limit, the OOM killer terminates the container, resulting in exit code 137. This is the most direct and common cause for this specific exit code in Vertex AI.
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 model has a disk I/O bottleneck.
Why it's wrong here
Exit code 137 signals SIGKILL from the OOM killer, so disk I/O cannot trigger it. Disk I/O tuning is tempting when a model stalls or times out on large reads, but that produces latency or exit code 1, not the kernel's memory-pressure kill.
- ✗
The model is using too much CPU.
Why it's wrong here
CPU issues would not cause SIGKILL with memory limit.
- ✗
The container image is incompatible with the machine type.
Why it's wrong here
An incompatible image typically fails immediately with an exec format or architecture error, not repeated SIGKILL after running. It is tempting because machine-type mismatches do break deployments, but those surface as startup failures, whereas 137 indicates runtime memory exhaustion.
- ✓
The model is using more memory than allocated (4GB).
Why this is correct
Exit code 137 signals SIGKILL, typically delivered by the Linux OOM killer when a container exceeds its memory cgroup limit. The 4GB allocation is therefore being breached by the model's runtime footprint, making memory exhaustion — not CPU, timeout, or network failure — the constraint the stem describes.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.