Databricks-ML-Pro ML Ops Practice Question
Your team is using MLflow Model Registry to manage a model that predicts customer churn. A new version has been registered and passed validation. You need to transition this version to the 'Production' stage and ensure that all downstream scoring jobs automatically use it. What should you do?
⚠ Common exam trap
Test-takers frequently confuse version-based referencing with stage-based referencing; only stage-based referencing enables automatic updates when a new version is promoted.
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 model version to 'Production' and update the downstream jobs to reference the model by stage 'Production'.
Transitioning the model version to the 'Production' stage and having downstream jobs reference the model by stage 'Production' is the correct approach. This allows you to promote new versions to production without modifying job code, ensuring that all consumers automatically use the latest approved model. It also maintains a clear audit trail and governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the model version in 'Staging' and update the downstream jobs to reference the model by stage 'Staging'.
Why it's wrong here
Keeping the model in 'Staging' means it is not officially in production. Downstream jobs referencing 'Staging' would use a non-production model, which is not appropriate for live scoring. This also bypasses the governance and validation steps associated with the 'Production' stage.
- ✓
Transition the model version to 'Production' and update the downstream jobs to reference the model by stage 'Production'.
Why this is correct
Transitioning to 'Production' and having jobs reference the stage ensures that when a new version is promoted, the jobs automatically pick it up. This is the intended workflow for stage-based model deployment in MLflow Model Registry, enabling seamless updates without code changes.
- ✗
Archive the current production model and register the new version with a new model name.
Why it's wrong here
Archiving and creating a new model name breaks the lineage and requires updating all downstream jobs to point to the new model. This is disruptive and unnecessary. The Model Registry is designed to manage multiple versions under the same model name and transition them through stages.
- ✗
Transition the model version to 'Production' and update the downstream jobs to reference the model by version number.
Why it's wrong here
Referencing by version number hardcodes the version, so downstream jobs will not automatically use a new version when it is promoted to 'Production'. This defeats the purpose of stage-based deployment and requires manual updates for each new version, increasing operational overhead and risk of errors.
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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.