Databricks-ML-Pro Model Development Practice Question
A data scientist has trained a model and wants to register it in the MLflow Model Registry on Databricks. They want to indicate that the model is ready for testing in a pre-production environment. Which stage should they transition the model version to?
⚠ Common exam trap
Many exam-takers confuse the Staging stage with Production or None, but Staging is the correct stage for pre-production testing.
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
✓
Staging
The MLflow Model Registry provides stages to manage model lifecycle. Staging is specifically meant for models that are undergoing testing or validation before production. By transitioning to Staging, the data scientist signals that the model is ready for pre-production evaluation, allowing stakeholders to test it without affecting live traffic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Archived
Why it's wrong here
Archived is used for models that are no longer active or have been retired. It is not a stage for testing; rather, it is a final state for deprecated models. Transitioning a new model to Archived would make it inactive and unsuitable for testing. This stage does not support the scenario's requirement for pre-production evaluation.
- ✓
Staging
Why this is correct
Staging is designed for models that are being tested or validated before production. It serves as a pre-production environment where the model can be evaluated with real or simulated traffic. Transitioning to Staging aligns with the goal of testing the model in a controlled setting before promoting it to Production. This is the correct stage for pre-production testing.
- ✗
None
Why it's wrong here
The None stage is the initial state of a model version when it is first registered. It indicates that the model has not been assigned a lifecycle stage. While a model can remain in None, it does not signify readiness for testing. The scenario explicitly asks for a stage that indicates readiness for pre-production testing, which is not None.
- ✗
Production
Why it's wrong here
The Production stage is intended for models that are actively serving live traffic. Transitioning a model to Production before it has been tested in a pre-production environment would be premature and risky. The scenario specifies that the model is ready for testing, not for full production deployment. Therefore, Production is not the appropriate stage.
About these practice questions
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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.