Databricks-ML-Pro ML Ops Practice Question
A regulated financial services firm must prove that every model promoted to production on Databricks is traceable and governed. They use Unity Catalog for models and MLflow for experiment tracking. Which two practices most directly satisfy an auditor's requirement to trace a production model version back to its training data and code? (Choose two.)
⚠ Common exam trap
The trap here is treating documentation fields like version descriptions or access permissions as equivalent to recorded data and code lineage.
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
✓
Enable the MLflow run's source notebook to be stored with the run so the exact code revision is retrievable from the run details.
Traceability requires machine-readable links from the production model version back to the exact data and code. Recording the dataset version and Git commit on the source run, plus preserving the run's source notebook, gives auditors a verifiable chain. Descriptions, request logging, and permissions document or control access but do not establish provenance, so they cannot satisfy the requirement alone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Grant the production service principal MANAGE on the registered model so it can update versions as needed.
Why it's wrong here
Privileges govern who may alter or administer the model; they do not record provenance. Giving the service principal MANAGE expands its authority without creating any link to training data or code, and it may even weaken governance by broadening write access. Permissions and traceability are separate concerns.
- ✓
Enable the MLflow run's source notebook to be stored with the run so the exact code revision is retrievable from the run details.
Why this is correct
MLflow records the source notebook or Git reference with each run, which lets reviewers retrieve the code that generated the model. Combined with the run-to-version link in the registry, this closes the loop from production model back to the authored code, which is precisely what a traceability audit expects.
- ✓
Log the training dataset path or Delta table version and the Git commit SHA as tags and parameters on the MLflow run that produced the registered model version.
Why this is correct
Tags and parameters on the source run create an explicit, queryable link between the registered version and the exact data and code used. An auditor can follow the model version to its run and read the recorded data version and commit. This is the core traceability artifact that ties a production model to its provenance.
- ✗
Increase the model version's stage description with a free-text summary of the business purpose and the approving manager's name.
Why it's wrong here
A textual description documents intent and approval but carries no machine-verifiable pointer to data or code. An auditor needs to reproduce or inspect the exact inputs, and prose cannot substitute for a recorded dataset version and commit. Descriptions are useful metadata but insufficient on their own for provenance.
- ✗
Configure the serving endpoint to log every request and response payload to a Delta table for later inspection.
Why it's wrong here
Request and response logging supports monitoring and drift analysis, but it captures inference traffic rather than training provenance. It says nothing about which dataset or code revision trained the model. While valuable for observability, it does not establish the lineage an auditor demands between the production version and its training inputs.
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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-ML-Pro 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-ML-Pro exam.