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Associate Data Practitioner

Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!

Fundamental

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Track Readiness

Set Exam Goal

Fundamental

Prepare and test your skills

Track Readiness

Set Exam Goal

Manage Ml Model Metadata Registry Provenance

Section: Organize models in Model Registry

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A data team is developing machine learning models in Google Cloud. To satisfy strict governance and regulatory compliance standards, the team must ensure complete reproducibility and auditability. They need a centralized approach to track model versions, record runtime parameters and evaluation metrics, and capture the end-to-end provenance linking training datasets, execution steps, and resulting model assets.

Which Google Cloud approach best satisfies these model metadata management and governance requirements?

Store serialized model weights in Cloud Storage buckets and rely on custom object metadata headers to document training runs and lineage.

Query BigQuery INFORMATION_SCHEMA views to automatically inspect model schemas, training parameters, and lineage relationships across projects.

Deploy models directly to serving endpoints and use Cloud Logging query filters as the primary system of record for model documentation.

Manage model versions and deployment in the Model Registry, while recording pipeline steps, workflow artifacts, and artifact lineage using Vertex ML Metadata.

Associate Data Practitioner

Manage Ml Model Metadata Registry Provenance

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