hardMultiple Select
PMLE Practice Question: A team is troubleshooting a Vertex AI Pipelines…
A team is troubleshooting a Vertex AI Pipelines run that keeps failing at the model evaluation step. The pipeline includes steps: data preprocessing, training, evaluation, and deployment. Which THREE actions should they take to diagnose the issue?
⚠ Common exam trap
Google Cloud often tests the misconception that resource scaling (Option C) is the first diagnostic step for pipeline failures, when in reality, most failures in Vertex AI Pipelines stem from misconfigured artifact passing or code errors, not hardware limits.
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
✓
Verify that the training step output is correctly linked as input to evaluation.
Option A is correct because Vertex AI Pipelines passes data between steps through declared input/output artifacts, so if the training step's output artifact is not correctly wired as the evaluation step's input, the evaluation component will fail from missing or malformed input. Option B is correct because running the evaluation code locally with the same input data isolates the component logic from the pipeline orchestration, quickly revealing whether the failure is in the code or in the pipeline configuration. Option D is correct because Cloud Logging captures the evaluation step's container stdout/stderr and error messages, which is the primary way to see the actual exception raised during the pipeline run. Option C is not appropriate because increasing memory is a speculative resource change that does not diagnose the root cause and would only help if an out-of-memory error were already confirmed. Option E is not appropriate because swapping the evaluation step for Vertex AI Model Evaluation changes the pipeline rather than diagnosing the existing failure, and it may not even be compatible with the current pipeline's inputs and outputs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Verify that the training step output is correctly linked as input to evaluation.
Why this is correct
Evaluation fails when its input artifact is missing or malformed. Confirming the training step's output model is correctly wired as the evaluation step's input verifies the pipeline graph passes a valid artifact, ruling out a broken dependency before debugging the code itself.
- ✓
Run the evaluation code locally with the same input data.
Why this is correct
Reproducing the evaluation step locally with identical input data isolates whether the failure is code or pipeline infrastructure. If it fails locally, the evaluation logic or data is faulty; if it passes, the issue lies in the Vertex AI Pipelines execution environment.
- ✗
Increase the memory of the evaluation step's machine.
Why it's wrong here
Premature fix without knowing if memory is the issue.
- ✓
Check the logs of the evaluation step in Cloud Logging.
Why this is correct
Cloud Logging captures the evaluation step's container stdout and stderr, including the exact exception and stack trace that caused the failure. Reading those logs identifies the precise error, which is faster than guessing at pipeline configuration causes.
- ✗
Replace the evaluation step with a Vertex AI Model Evaluation service.
Why it's wrong here
Changes the process instead of diagnosing the current failure.
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