Databricks-GenAI-Assoc Governance Practice Question
A data scientist wants to use a Unity Catalog registered model in a GenAI pipeline. The model was trained on sensitive data, and the governance team requires that the model's lineage back to the training dataset be traceable. Which Unity Catalog capability provides this traceability?
⚠ Common exam trap
The trap here is assuming that metadata such as tags or signatures can substitute for automated lineage when proving traceability to an auditor.
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
✓
Unity Catalog lineage graph that automatically tracks data and model dependencies.
Unity Catalog lineage automatically tracks the flow of data through notebooks, jobs, and models, including the training dataset used to produce a model version. This gives the governance team an auditable, automated trace from model to data. Manual methods such as tags or comments are not reliable for 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.
- ✓
Unity Catalog lineage graph that automatically tracks data and model dependencies.
Why this is correct
Unity Catalog automatically captures lineage for tables, models, and notebooks, showing how a model version was derived from training datasets. This provides the required traceability without manual effort and is auditable. The lineage graph is a core governance feature that links data assets across the workspace.
- ✗
Model version tags that describe the training data.
Why it's wrong here
Tags are user-defined metadata and can be applied manually, but they do not automatically capture lineage. Relying on tags alone is error-prone and cannot guarantee traceability. The governance team requires an automated, auditable link between the model and its training data, which tags do not provide.
- ✗
Workspace notebook comments that document the training data path.
Why it's wrong here
Notebook comments are free-form text and are not enforced or automatically captured as lineage. They can be outdated or missing, and they do not provide an auditable link. The governance team needs automated lineage, not manual documentation, to ensure traceability.
- ✗
Model signature that records input and output schema.
Why it's wrong here
The model signature describes the expected input and output columns for inference, not the training data source. It is useful for validation but does not provide lineage back to the training dataset. Therefore, it cannot satisfy the governance team's traceability requirement.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.