The answer is to set the 'machineType' field for the training task to a higher memory machine, such as n1-highmem-8. This is correct because exit code 137 in Vertex AI Pipelines directly indicates that the container was killed by the Linux OOM killer due to insufficient memory, and the default allocation for custom training containers is typically only 4 GiB. By specifying a machine type with more memory, the container automatically receives a larger memory limit without any code changes, which perfectly satisfies the constraint of not modifying the application code. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI resource configuration versus application-level optimization—a common trap is trying to adjust container memory limits via environment variables or Docker settings, but the correct approach is to change the machine type at the pipeline task definition. Remember the mnemonic: "137 means memory heaven—upgrade the machine, not the code."
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A pharmaceutical company uses Vertex AI Pipelines with custom training containers. Recently, the pipeline has been failing with 'Container failed with exit code 137' (out of memory). The container runs with default memory limit. The team needs to fix this without changing the code. The project quota for CPU and memory is sufficient. What should the team do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the 'machineType' field for the training task to a higher memory machine.
Option B is correct because the container is running out of memory (exit code 137) with the default memory limit. In Vertex AI Pipelines, when using custom training containers, the default memory allocation is typically 4 GiB. By setting the 'machineType' field to a higher memory machine (e.g., n1-highmem-8), the container automatically receives more memory without requiring code changes. This directly resolves the OOM issue while respecting the constraint of not modifying the code.
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.
✗
Add a resource hint to the container spec for more memory.
Why it's wrong here
Vertex AI pipeline tasks do not use resource hints; memory is specified via machine type.
✓
Set the 'machineType' field for the training task to a higher memory machine.
Why this is correct
This directly provides more memory to the container without code changes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the model parallelism by using multi-worker training.
Why it's wrong here
This would likely increase memory usage, not solve the out-of-memory issue.
✗
Use a smaller dataset for training.
Why it's wrong here
The requirement is not to change the code or data; using a smaller dataset would change the training process.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that resource hints or environment variables can override default memory limits in Vertex AI Pipelines, but the correct mechanism is the 'machineType' field in the task specification, not hints or code changes.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines uses the 'machineType' field in the ContainerSpec to define the compute resources for a training step. The default machine type (e.g., n1-standard-4) provides 4 vCPUs and 15 GB of memory, but custom containers may require more. Exit code 137 is a Linux kernel OOM killer signal, indicating the container exceeded its memory limit. Changing to a high-memory machine type (e.g., n1-highmem-16 with 104 GB) directly increases the container's memory allocation without code changes. In real-world scenarios, teams often misconfigure resource limits in YAML pipeline definitions, leading to silent OOM failures that are resolved by adjusting the machine type.
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.
Automating and orchestrating ML pipelines — This question tests Automating and orchestrating ML pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the 'machineType' field for the training task to a higher memory machine. — Option B is correct because the container is running out of memory (exit code 137) with the default memory limit. In Vertex AI Pipelines, when using custom training containers, the default memory allocation is typically 4 GiB. By setting the 'machineType' field to a higher memory machine (e.g., n1-highmem-8), the container automatically receives more memory without requiring code changes. This directly resolves the OOM issue while respecting the constraint of not modifying the code.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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