The answer is exit code 137, which signals that the model’s container was terminated by the Linux kernel’s Out-Of-Memory (OOM) killer. This occurs because Vertex AI Prediction allocates a fixed memory limit—defaulting to 4GB for custom containers—and when your model’s inference process exceeds that allocation, the OOM killer forcibly stops the container, producing exit code 137. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI’s resource constraints and how to interpret container exit codes; a common trap is assuming the issue is a code bug or a timeout, when the real culprit is memory pressure. To remember this, think of the mnemonic “137 = 1 process, 3 kills, 7 OOM” — the kernel sends signal 9 (SIGKILL) to the process, and the exit code 137 is simply 128 + 9, the standard Unix representation for a SIGKILL termination.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Disk I/O would not cause SIGKILL.
✗
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
Incompatibility would cause different errors.
✓
The model is using more memory than allocated (4GB).
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Exit code 137 corresponds to SIGKILL (signal 9) sent by the Linux kernel's OOM killer when a process exceeds its memory limit (cgroup memory.max). In Vertex AI, the default memory limit for a custom container is 4GB, but this can be adjusted via the `machineSpec` or `containerSpec` in the deployment configuration. A real-world scenario is a model that loads large embeddings or caches inference results in memory, gradually consuming all available RAM until the OOM killer intervenes.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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