Databricks-ML-Assoc Databricks Machine Learning Practice Question
What is the primary function of the 'Model Registry' in the Databricks ML ecosystem?
⚠ Common exam trap
Candidates confuse the Model Registry with the MLflow Tracking UI. Tracking is for experiment metadata, whereas the Registry is specifically for managing the deployment lifecycle and model promotion stages.
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
✓
To provide a centralized platform for model lifecycle management
The Model Registry provides a centralized hub for managing the full lifecycle of a machine learning model. It allows teams to track versions, manage model stages (Staging, Production, Archived), and facilitate collaboration. By serving as a 'source of truth' for model status, it ensures that stakeholders can confidently transition models from development to production while maintaining complete audit trails and lineage of all changes applied to the model artifacts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To store raw training datasets for feature engineering
Why it's wrong here
The Model Registry is designed for model artifacts and metadata, not for storing training data. Raw datasets and feature data are stored in the Databricks Feature Store or directly in Delta tables, ensuring a clear separation of concerns between the data layer and the model management layer.
- ✓
To provide a centralized platform for model lifecycle management
Why this is correct
The Model Registry acts as the central governance point for models. It provides functionalities for versioning, stage transitions (e.g., Staging to Production), and centralized model documentation, which is essential for ensuring that only approved, validated models are deployed into production environments within a controlled organizational workflow.
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To execute distributed hyperparameter tuning experiments
Why it's wrong here
Hyperparameter tuning is typically performed using libraries like Hyperopt or Ray within a notebook or job context. The Model Registry does not perform execution or compute tasks; it is a repository for storing the resulting models produced by these tuning processes, not the engine for tuning itself.
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To monitor real-time inference drift in production
Why it's wrong here
Model monitoring for drift is handled by Databricks Model Monitoring or external observability tools. While the registry holds the model version, it is not the tool responsible for the continuous analysis of inference data or the generation of alerts regarding statistical changes in production model outputs.
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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-Assoc 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-Assoc exam.