Exam Professional Machine Learning Engineer topic 1 question 305 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 305
Topic #: 1

[All Professional Machine Learning Engineer Questions]

Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?

A. Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
B. Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.
C. Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
D. Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.

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Suggested Answer: A 🗳️

by a38a239 at May 4, 2025, 11:38 a.m.

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hit_cloudie

4 days, 19 hours ago

Selected Answer: C

Vertex AI Experiments captures model artifacts and training runs. Vertex ML Metadata tracks lineage, parameters, and outputs. This is native, simple, and reusable within Vertex AI workflows.

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a38a239

3 weeks ago

Selected Answer: A

Vertex AI Pipelines automatically logs every component’s inputs, outputs, parameters, and artifacts into the built‑in ML Metadata store, giving you end‑to‑end lineage for data, models, and evaluation results. The Vertex AI SDK lets you programmatically log any extra metadata—git commit hashes, container image URIs, custom evaluation reports—directly into the same metadata store from within your training or preprocessing code.

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