Databricks-ML-Pro Model Development Practice Question
Which THREE features are provided by the Databricks Model Registry for model lifecycle management?
⚠ Common exam trap
Test-takers often select hyperparameter tuning or data preprocessing options, confusing operational tracking features with the Model Registry's lifecycle governance tools.
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
✓
Transitioning models between stages (e.g., Staging to Production).
The Model Registry provides the governance layer for machine learning, allowing teams to track stages, version history, and approval workflows. These features are necessary to transition from an experimental notebook-based workflow to a production environment where changes to models are managed, tested, and audited, ensuring that only validated artifacts are promoted to critical business-facing applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Transitioning models between stages (e.g., Staging to Production).
Why this is correct
Stage transitions are central to the CI/CD model deployment workflow. They allow organizations to promote models through a well-defined lifecycle, ensuring that models in 'Production' have passed all necessary testing, validation, and approval steps, which is a requirement for enterprise-grade deployment and risk management in machine learning.
- ✗
Automatic retraining of models on a fixed daily schedule.
Why it's wrong here
The Model Registry manages models, not model training schedules. Retraining logic should be implemented via Databricks Jobs or Workflows, which can trigger training runs. The Registry does not have built-in orchestration capabilities to execute code periodically; it only stores and manages the resulting artifacts and their metadata.
- ✓
Tracking version history of registered models.
Why this is correct
Versioning is essential for auditability and rollback. By maintaining a history of model versions, the registry allows developers to track changes, compare performance across different iterations, and quickly revert to a previous, known-good version if a model performs poorly in production, ensuring system stability and operational resilience.
- ✗
Automated deployment of models to edge devices like mobile phones.
Why it's wrong here
Databricks Model Registry manages models for cloud-based inference services (Model Serving) and batch inference. It is not designed to push artifacts to mobile edge devices, which require different distribution mechanisms, SDKs, and hardware-specific compilation processes that fall outside the scope of Databricks-native model deployment services.
- ✓
Commenting and collaboration on specific model versions.
Why this is correct
Collaboration features allow data scientists and reviewers to discuss model performance, provide feedback on validation results, and document decisions within the registry itself. This centralized communication is vital for maintaining a clear audit trail and facilitating team alignment throughout the model approval process prior to production deployment.
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.