Databricks-ML-Assoc Databricks Machine Learning Practice Question
Which Databricks component should be used to track parameters, code versions, metrics, and output files when running machine learning experiments?
⚠ Common exam trap
Candidates often confuse MLflow Model Registry with MLflow Tracking, selecting the registry for experiment logging when Tracking is specifically designed for parameters, metrics, and code versioning.
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 Tracking
MLflow Tracking is the primary component for logging the inputs and outputs of machine learning runs. It allows data scientists to organize experiments and compare performance across different iterations. By centralizing this information, teams can ensure reproducibility and visibility into the model development process, which is foundational for maintaining high-quality machine learning workflows and facilitating collaboration within the Databricks environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks Model Registry
Why it's wrong here
The Model Registry is used for managing the lifecycle of models, including versioning and staging, after they have been trained. It is not the correct tool for logging experiment metrics or parameters during the initial exploration and training phases, which are handled by the MLflow Tracking service instead.
- ✓
MLflow Tracking
Why this is correct
MLflow Tracking provides the API and UI to log parameters, code versions, metrics, and artifacts during model training. It is the essential tool for managing experimental data in Databricks, enabling users to keep track of their progress and compare different model versions during the training and hyperparameter tuning cycles.
- ✗
Databricks Feature Store
Why it's wrong here
The Feature Store is designed for creating, sharing, and managing machine learning features to ensure consistency across training and serving. It does not track experiment metrics or parameters. Using it for experiment tracking would be an incorrect application of the tool's purpose and would fail to capture necessary metadata.
- ✗
Unity Catalog
Why it's wrong here
Unity Catalog is the governance solution for data, analytics, and AI assets in Databricks. While it can govern models and datasets, it does not provide the specific tracking capabilities for parameters and metrics that are required for day-to-day model experimentation and iterative machine learning development in the workspace.
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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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