Databricks-ML-Assoc ML Workflows Practice Question
A data scientist is comparing multiple hyperparameter configurations for a model and wants to view the resulting metrics side by side in a single interface, sort runs by accuracy, and drill into individual run details. Which MLflow component provides this capability?
⚠ Common exam trap
The trap here is assuming the Model Registry UI or Jobs run history can compare experiment runs, when only the MLflow Tracking UI provides that side-by-side metric view.
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, which lists runs within an experiment and supports sorting and filtering by metrics.
The MLflow Tracking UI is designed for experiment comparison. It presents runs in a sortable table with metrics and parameters, supports filtering, and allows drilling into individual runs. The Model Registry UI, Projects, and Jobs run history serve different purposes and do not offer the same run comparison and metric sorting features.
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 Tracking UI, which lists runs within an experiment and supports sorting and filtering by metrics.
Why this is correct
The MLflow Tracking UI is the built-in interface for viewing runs in an experiment. It displays metrics, parameters, and tags in a sortable table, allows filtering, and lets users click into a run to see artifacts and details. This directly supports comparing hyperparameter configurations side by side and sorting by accuracy.
- ✗
MLflow Model Registry UI, which shows model versions and their stage transitions.
Why it's wrong here
The Model Registry UI focuses on model versions, stages, and annotations for lifecycle management. It does not display per-run metrics for hyperparameter comparison or allow sorting runs by accuracy. While it can show the source run of a model version, it is not the primary interface for comparing experimental runs.
- ✗
MLflow Projects dashboard, which lists available projects and their entry points.
Why it's wrong here
MLflow Projects do not have a dedicated dashboard for comparing runs. Projects are defined by MLproject files and are executed via the CLI or API. They do not provide a UI for sorting metrics or drilling into run details, so they cannot satisfy the comparison requirement.
- ✗
Databricks Jobs run history, which shows task durations and statuses for scheduled workflows.
Why it's wrong here
Jobs run history is oriented toward orchestration, showing task success, failure, and duration. It does not surface MLflow metrics, parameters, or artifacts for model comparison. Using it to compare hyperparameter configurations would not provide the side-by-side metric view or sorting capabilities needed.
About these practice questions
This Databricks-ML-Assoc question is part of Courseiva's 319-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.