Databricks-ML-Assoc Model Development Practice Question
When using MLflow to manage the lifecycle of a model in Databricks, why should you use the Model Registry instead of just saving model files to DBFS?
⚠ Common exam trap
Candidates often treat DBFS as a production-ready model management system, failing to recognize that it lacks the critical versioning and stage-gating features of the Model Registry.
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
✓
It allows for versioning and stage management of models.
The Model Registry provides versioning, stage transitions (e.g., Staging to Production), and centralized access control, which are essential for governed model deployment. Directly saving to DBFS ignores these enterprise features, making it impossible to track lineage or manage model lifecycle status effectively. Using the Registry creates an audit trail and standardizes the deployment workflow, which is critical for compliance and ensuring that only validated models move into production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It automatically compresses the model files to save storage space.
Why it's wrong here
The Model Registry does not perform file compression. Serialization is handled by the specific model flavor (e.g., scikit-learn, TensorFlow) during the logging process. The Registry's primary function is to provide an organizational layer and lifecycle management for models, not to perform file system operations or data compression tasks.
- ✓
It allows for versioning and stage management of models.
Why this is correct
The Model Registry offers full versioning, allowing teams to roll back to previous versions if needed. It also supports transition states like 'Staging' and 'Production,' which are key for CI/CD pipelines, enabling organizations to manage the model lifecycle with clear rules, auditability, and governance over which model is currently active.
- ✗
It allows models to be trained directly on the registry.
Why it's wrong here
Models are trained in notebooks or jobs, not within the Registry. The Registry is a storage and management repository for artifacts that have already been trained. It has no compute capacity and is not capable of executing model training code, as that responsibility belongs to the Databricks Spark clusters.
- ✗
It converts models into a proprietary format for faster inference.
Why it's wrong here
The Model Registry does not change the internal format of the model. It stores the artifacts exactly as they were logged by the MLflow client. Any performance optimizations for inference, such as model conversion or quantization, must be performed by the developer before or during the initial model logging process.
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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-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.