Databricks-ML-Pro ML Ops Practice Question
A data scientist is using MLflow Tracking to log experiments for a model that predicts customer lifetime value. They want to compare runs across multiple experiments and identify the best performing run based on a custom metric called 'rmse'. Which MLflow feature should they use?
⚠ Common exam trap
A common mix-up: candidates confuse MLflow Tracking with MLflow Model Registry, which manages model lifecycle but not run comparison.
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 UI and API
MLflow Tracking provides both a UI and an API to log, query, and compare runs. The UI allows visual comparison of metrics like 'rmse' across experiments, and the API supports programmatic search and filtering. This makes it the appropriate feature for identifying the best performing run based on a custom metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MLflow Models
Why it's wrong here
MLflow Models is a standard format for packaging models for deployment. It does not include functionality for comparing runs or metrics. While models are logged as artifacts, the comparison of runs is handled by the Tracking component.
- ✗
MLflow Model Registry
Why it's wrong here
MLflow Model Registry is for managing model versions and stages, not for comparing experiment runs. It does not provide metric comparison across runs. The data scientist needs to analyze runs before registering a model, so the registry is not the right tool.
- ✗
MLflow Projects
Why it's wrong here
MLflow Projects are used to package code for reproducibility, not for comparing runs. They do not provide a UI or API for cross-experiment metric comparison. The data scientist needs a way to query and visualize metrics, which Projects do not offer.
- ✓
MLflow Tracking UI and API
Why this is correct
The MLflow Tracking UI allows users to visualize and compare runs across experiments, including metrics like 'rmse'. The API (e.g., MlflowClient.search_runs) enables programmatic querying and filtering based on metrics. This is the correct tool for comparing runs and identifying the best one.
About these practice questions
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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.