Databricks-ML-Pro ML Ops Practice Question
Which component of Databricks ML is best suited for tracking model hyperparameters, metrics, and code versions during the experimentation phase of the ML lifecycle?
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 dedicated component designed to record every experiment's parameters, metrics, and code state. By capturing these elements, data scientists can compare different iterations of a model to identify the best-performing configuration. This visibility is vital in MLOps, as it creates an audit trail of how a final model was derived, supporting reproducibility and informed decision-making before promoting a model to the registry.
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 SQL
Why it's wrong here
Databricks SQL is intended for data warehousing, reporting, and BI tasks, not for tracking machine learning experiments. While it can query logs, it lacks the specific MLflow integration required to log parameters and metrics directly from the model training process.
- ✓
MLflow Tracking
Why this is correct
MLflow Tracking provides the API and UI to record experiments. It allows developers to log parameters, performance metrics, and artifacts like serialized models and plots, which are essential for comparing runs and selecting the optimal model for production deployment.
- ✗
Delta Lake
Why it's wrong here
Delta Lake is an open-source storage layer that brings reliability to data lakes through ACID transactions. While it stores the data used for training, it does not provide the model-specific tracking functionality like parameter logging or model versioning needed for the experimentation lifecycle.
- ✗
Unity Catalog
Why it's wrong here
Unity Catalog is a governance solution for data and AI assets. While it manages access and discovery, it is not an experiment tracking tool. It does not provide the capability to log training hyperparameters or compare different model runs during the iterative model development phase.
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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.