PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An ML team uses Vertex AI Pipelines and wants to automatically generate model cards documenting model purpose, evaluation results, and intended use. Which approach should they take?
⚠ Common exam trap
PMLE often tests the distinction between metadata storage and model card generation — candidates pick Cloud Data Catalog or custom BigQuery tables because they store metadata, but only Vertex AI Model Registry generates the structured model card.
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 Model Registry to generate model cards automatically from model metadata.
Vertex AI Model Registry automatically generates model cards from model metadata, including purpose, evaluation results, and intended use, when models are registered and their metadata is populated. This provides a native, integrated way to document models without manual effort or custom components. It aligns with Vertex AI's governance and documentation features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually create a Google Doc and share it with the team.
Why it's wrong here
A shared Google Doc cannot be generated by pipeline components, so model purpose and evaluation results are never captured automatically at run time. Manual documentation suits one-off research models, but Vertex AI Pipelines requires programmatic artefact generation via the model card toolkit or metadata tracking.
- ✗
Use Cloud Data Catalog to annotate the model artifact.
Why it's wrong here
Cloud Data Catalog annotates and discovers data assets; it does not generate model card documents containing evaluation results and intended use. It is tempting for governance and lineage, and would be correct for cataloguing datasets, but model card generation requires the Model Card Toolkit within the pipeline.
- ✗
Write a custom Kubeflow Pipelines component that creates a BigQuery table with model metadata.
Why it's wrong here
A BigQuery table stores metadata rows but produces no model card artefact, so purpose and intended-use fields are never rendered. Custom components suit bespoke metadata schemas; automatic model cards need the dedicated Model Card Toolkit or Vertex ML Metadata integration.
- ✓
Use Vertex AI Model Registry to generate model cards automatically from model metadata.
Why this is correct
Vertex AI Model Registry generates model cards from registered model metadata, including purpose, evaluation results and intended use, when the model is uploaded with those details. This automates documentation from the pipeline's existing metadata rather than requiring manual authoring.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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