PMLE Scaling Prototypes into ML Models Practice Question
An engineer is training a model on Vertex AI using a custom container. The training job fails with an error indicating that the container exited with a non-zero status. The engineer wants to debug the issue. What is the best way to access the logs?
⚠ Common exam trap
A common trap is the misconception that you can SSH into a training container or that logs are stored in Cloud Storage, when in fact Cloud Logging is the centralized, default logging solution for all Vertex AI training jobs.
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
✓
Check the logs in Cloud Logging (Logs Explorer)
Vertex AI automatically streams all container stdout and stderr to Cloud Logging (Logs Explorer). When a custom container exits with a non-zero status, the detailed error messages, stack traces, and application logs are captured there, making it the primary and most comprehensive debugging tool. Cloud Logging provides structured, searchable logs without requiring direct access to the container.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SSH into the training container using Vertex AI's SSH feature
Why it's wrong here
Vertex AI training containers run without an SSH daemon and terminate once the job exits, so no shell session is possible. It is tempting because SSH suits debugging long-running VMs, but the correct approach reads the container's stdout and stderr in Cloud Logging.
- ✗
View logs in Cloud Storage under the job's output directory
Why it's wrong here
Vertex AI writes container stdout and stderr to Cloud Logging, not to the job's Cloud Storage output directory, which holds artefacts such as the saved model. It is tempting because the output directory is job-specific, but it contains no runtime logs, so the failure cause stays hidden.
- ✗
Use Cloud Debugger to inspect the container
Why it's wrong here
Cloud Debugger attaches to live application processes, and training containers terminate on non-zero exit, so no debuggable process remains. It is tempting because it inspects running code, but the correct approach reads the job's logs in Cloud Logging, which captures the container's stdout and stderr.
- ✓
Check the logs in Cloud Logging (Logs Explorer)
Why this is correct
Vertex AI writes custom container training output, including stderr and the non-zero exit trace, to Cloud Logging. Logs Explorer surfaces those entries for the failed job, satisfying the debugging requirement by exposing the container's actual error rather than only the job's status.
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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.