Exam Professional Machine Learning Engineer topic 1 question 305 discussion

Juntos, Pipelines + ML Metadata proporcionan una solución nativa, reutilizable y con trazabilidad completa.

Use the native, purpose-built orchestration service (Pipelines) via its SDK.

I'm going with A. C is redundant. Vertex AI Experiments is a higher-level concept that organizes groups of pipeline or training runs for comparative analysis. It relies on the underlying Vertex ML Metadata service, which tracks artifacts and lineage automatically when using pipelines. You wouldn't use them as separate systems for different purposes.

A is correct. C is wrong because artifacts are tracked in ML Metadata (and surfaced via Experiments), not tracked exclusively in Experiments. Metadata tracks both artifacts and lineage.

C --> Vertex AI Experiments tracks model executions and artifacts. Vertex ML Metadata tracks model lineage. A - is not reusable solution. B - MLflow is not Google's solution, so it's less simple to implement. D - this is custom solutions, so less simple

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.

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.