Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using MLflow to track experiments on Databricks. They want to compare multiple runs and identify the best performing model based on a custom metric. Which TWO features of MLflow can be used to achieve this? (Choose two.)
⚠ Common exam trap
Candidates often confuse MLflow features that manage models or automate logging with those that actually compare runs and sort by metrics.
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 API's search_runs method
The MLflow UI's compare runs feature and the tracking API's search_runs method both allow you to compare runs and sort by custom metrics. The UI provides a visual side-by-side comparison, while search_runs enables programmatic querying and sorting. Together, they enable 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 tracking API's search_runs method
Why this is correct
The MLflow tracking API provides the search_runs method, which allows programmatic querying of runs filtered and sorted by metrics. You can specify an order_by clause on a custom metric to retrieve the top-performing runs. This enables automated comparison and selection of the best model based on the custom metric.
- ✓
MLflow UI's compare runs feature
Why this is correct
The MLflow UI's compare runs feature allows you to select multiple runs and view their metrics, parameters, and artifacts side by side. You can sort runs by a custom metric to quickly identify the best performer. This directly satisfies the requirement to compare runs and select the best model based on a custom metric.
- ✗
MLflow Projects with a custom entry point
Why it's wrong here
MLflow Projects are used to package code for reproducibility and execution, not for comparing runs or identifying the best model. While you can run projects, they do not provide built-in comparison or sorting capabilities. Thus, they are not suitable for the task of comparing runs based on a custom metric.
- ✗
MLflow Model Registry's stage transitions
Why it's wrong here
The Model Registry manages model versions and stages (e.g., Staging, Production), but it does not compare runs or sort by custom metrics. Stage transitions are for deployment lifecycle management, not for run comparison. Therefore, it does not fulfill the requirement to compare multiple runs and identify the best model.
- ✗
MLflow's automatic logging with autolog()
Why it's wrong here
Automatic logging captures parameters, metrics, and artifacts without manual instrumentation, but it does not provide comparison or ranking of runs. You still need to use the UI or API to compare runs. Autolog simply records data; it does not analyze or select the best model, so it is not a comparison feature.
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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.