Databricks-ML-Assoc Model Deployment Practice Question
Which Databricks feature allows you to manage the lifecycle of a model, including transitions from 'Staging' to 'Production'?
⚠ Common exam trap
Candidates often confuse workspace git repositories or MLflow tracking servers with the specific component dedicated to lifecycle stage transitions like 'Staging' to 'Production'.
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
✓
MLflow Model Registry
The MLflow Model Registry provides a centralized store for managing models throughout their lifecycle. It allows teams to track model versions, apply transitions (e.g., Staging to Production), and manage metadata. This central control is fundamental for MLOps, as it provides a clear record of which model is currently active in the production environment and ensures that only validated models are promoted to downstream systems.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Databricks SQL
Why it's wrong here
Databricks SQL is intended for data warehousing and business intelligence queries. It does not provide the model-specific functionality required to manage ML models, such as version control, lifecycle transitions, or model artifact storage. It is the wrong tool for managing the machine learning development lifecycle and deployment process.
- ✓
MLflow Model Registry
Why this is correct
The MLflow Model Registry is the purpose-built service in Databricks for managing the entire model lifecycle. It allows for versioning, metadata tracking, and stage transitions, providing an organized approach to moving models from initial experimentation through staging to final deployment in a production serving endpoint.
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Delta Live Tables
Why it's wrong here
Delta Live Tables is a framework for building reliable and maintainable data pipelines. While it is excellent for processing data that might feed into an ML model, it does not have the capability to store, version, or transition ML models. It is focused on data engineering rather than ML model management.
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Unity Catalog
Why it's wrong here
While Unity Catalog provides governance for models, the specific functionality for lifecycle management and stage transitions is handled by the MLflow Model Registry integration within Unity Catalog. Simply having Unity Catalog is not the management feature itself; it serves as the underlying governance layer for the registry.
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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
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