Databricks-GenAI-Assoc Application Development Practice Question
An AI engineer is using MLflow to track experiments for a generative AI application. They want to log parameters, metrics, and artifacts for each run, and later compare runs to select the best model. Which MLflow component should they use to organize runs into a named group for a specific project?
⚠ Common exam trap
Candidates often confuse the organizational unit (Experiment) with the execution unit (Run) or the model management component (Model Registry), which serve different purposes.
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 Experiment
MLflow Experiments are designed to group runs for a specific project. They allow you to log and compare multiple runs, making it easy to track progress and select the best model. The experiment is the primary organizational unit in MLflow, and it is where runs are created and stored.
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 Model Registry
Why it's wrong here
The Model Registry is used to manage the lifecycle of models, including versioning, stage transitions, and annotations. It is not used to organize runs during experimentation. While you can register a model from a run, the Registry is for post-training model management, not for grouping runs for comparison.
- ✗
MLflow Run
Why it's wrong here
An MLflow Run is a single execution of a model training or evaluation script. It captures parameters, metrics, and artifacts for that specific execution. Runs are contained within an experiment. Using a Run alone does not provide a named group for a project; you need an experiment to group multiple runs.
- ✓
MLflow Experiment
Why this is correct
An MLflow Experiment is a logical grouping of runs for a specific project or objective. It allows you to organize and compare runs, view metrics, and manage artifacts. By creating an experiment, you can log multiple runs and later query them to find the best model based on metrics. This is the core organizational unit in MLflow.
- ✗
MLflow Tracking Server
Why it's wrong here
The MLflow Tracking Server is a backend service that stores and serves experiment data. It is infrastructure, not an organizational unit. You use it to host experiments and runs, but it does not itself group runs into a named project. The grouping is done by experiments within the tracking server.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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