Databricks-ML-Assoc Databricks Machine Learning Practice Question
A team wants to compare multiple model runs for a fraud detection project, view their metrics side by side, and identify which run produced the best area under the ROC curve. They have already logged each run with MLflow. Which Databricks capability should they use to perform this comparison?
⚠ Common exam trap
Candidates often confuse infrastructure or governance logs with the experiment UI that actually surfaces run 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
✓
The MLflow experiment runs comparison view in the workspace.
MLflow experiments collect runs, and the experiment comparison view lets users sort, filter, and visualize metrics across runs. For a fraud detection project where runs are already logged, this view provides the side-by-side metric comparison needed to identify the best area under the ROC curve without additional tooling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cluster event log filtered by Spark stage failures.
Why it's wrong here
Cluster event logs track infrastructure events such as node starts and Spark stage failures, not model evaluation metrics. They contain no information about area under the ROC curve or run parameters. Using them to compare model performance would be ineffective because the data needed for model comparison is not recorded there at all.
- ✓
The MLflow experiment runs comparison view in the workspace.
Why this is correct
MLflow experiments group related runs, and the experiment UI provides a comparison view where metrics such as area under the ROC curve can be sorted and charted across runs. This directly supports side-by-side evaluation and selecting the best-performing run, which is exactly the fraud detection team's goal after logging runs with MLflow.
- ✗
The Delta transaction log for the training data table.
Why it's wrong here
The Delta transaction log records table-level operations such as writes, commits, and schema changes. It does not store model metrics or run parameters, so it cannot show which run produced the best area under the ROC curve. It is relevant to data versioning, not to comparing MLflow experiment runs.
- ✗
The Unity Catalog lineage graph for the feature tables.
Why it's wrong here
Unity Catalog lineage shows relationships between data assets such as tables, notebooks, and models, but it does not present run-level metrics for comparison. It cannot rank runs by area under the ROC curve. While useful for governance and impact analysis, it does not fulfill the requirement of comparing logged runs side by side.
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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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