Databricks-ML-Pro ML Ops Practice Question
Your team uses MLflow Model Registry. A model version currently in Production has a critical flaw and must be rolled back to a previous version. The previous version is in the Archived stage. What is the most operationally sound approach to restore service quickly while preserving the audit trail?
⚠ Common exam trap
The trap here is assuming that a rollback requires creating a new model version or deleting the bad one, when stage transitions alone can restore service while preserving 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
✓
Transition the flawed Production version to Archived, then transition the previous version from Archived to Production.
The correct approach is to archive the problematic Production version and promote the prior known-good version back to Production. This restores service using an already-validated artifact while maintaining full stage-transition history for audit and rollback traceability. Deleting versions, creating new models, or bypassing the registry all sacrifice governance or introduce unnecessary operational risk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a new registered model with the previous version's artifacts and point the serving endpoint to it.
Why it's wrong here
Creating a separate registered model fragments the model lifecycle and breaks the established lineage under a single model name. Serving endpoints and downstream consumers that reference the original model name would need reconfiguration, increasing risk and downtime. It also duplicates artifacts unnecessarily and complicates future version management.
- ✓
Transition the flawed Production version to Archived, then transition the previous version from Archived to Production.
Why this is correct
Archiving the flawed version removes it from active serving while retaining its metadata and lineage, and moving the prior version back to Production restores the known-good artifact. This preserves a complete audit trail of stage transitions, which is essential for governance and post-incident review.
- ✗
Delete the flawed Production model version and re-register the previous version as a new model version.
Why it's wrong here
Deleting the flawed version destroys audit evidence and breaks lineage, which is unacceptable for governance. Re-registering creates a new version number, complicating traceability and potentially invalidating references in downstream systems. This approach also risks losing the original run metadata tied to the deleted version.
- ✗
Leave the flawed version in Production and instead update the serving endpoint to load the previous version by its run ID.
Why it's wrong here
Bypassing the Model Registry stage to load by run ID undermines the registry as the source of truth and leaves a known-bad version marked as Production. This creates confusion for consumers and audit failures, and the endpoint configuration may not support arbitrary run ID references cleanly, risking further downtime.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.