PMLE Collaborating to manage data and models Practice Question
Your team uses Vertex AI Pipelines to automate the training and deployment of a recommendation model. The pipeline includes a step that evaluates the model and only deploys it if the evaluation metric exceeds a threshold. You need to ensure that the pipeline's artifacts, including the evaluation metrics and the deployed model, are tracked and can be traced back to the pipeline run for auditing. What should you do?
⚠ Common exam trap
The trap here is assuming that manual logging or Cloud Logging can substitute for ML Metadata's automatic lineage tracking, when only ML Metadata provides a queryable artifact graph.
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
✓
Use Vertex AI ML Metadata to log the evaluation metrics and model artifacts, and associate them with the pipeline run.
Vertex AI ML Metadata provides automatic lineage tracking for pipeline artifacts, including metrics and models. It records relationships between executions and artifacts, enabling auditing and reproducibility. Other methods lack the structured, integrated metadata store that ML Metadata offers, making them less reliable for tracing and compliance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the pipeline to send an email with the evaluation metrics and model details to a distribution list for record-keeping.
Why it's wrong here
Email is not a structured or queryable audit trail. It does not integrate with Vertex AI's metadata store and cannot be used to trace artifacts programmatically. This method is not suitable for compliance or reproducibility requirements.
- ✗
Enable Cloud Logging for the pipeline and rely on the logs to capture the evaluation metrics and model deployment events.
Why it's wrong here
Cloud Logging captures logs but is not designed for artifact lineage. Logs are unstructured and may not persist as long as needed for auditing. They also do not provide a graph of relationships between pipeline runs, metrics, and models, making traceability difficult.
- ✗
Store the evaluation metrics in a BigQuery table and the model in Cloud Storage, and record the pipeline run ID in both locations.
Why it's wrong here
While this creates a manual link via the pipeline run ID, it does not provide automatic lineage tracking. You would need to query both systems separately, and there is no guarantee that the artifacts are correctly associated. This approach is error-prone and lacks the integrated metadata view that ML Metadata offers.
- ✓
Use Vertex AI ML Metadata to log the evaluation metrics and model artifacts, and associate them with the pipeline run.
Why this is correct
Vertex AI ML Metadata automatically tracks artifacts, executions, and contexts for Vertex AI Pipelines runs. By logging metrics and model artifacts, you create a lineage that links them to the pipeline run. This enables auditing and reproducibility, as you can trace which run produced which model and its metrics.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.