Databricks-ML-Pro Model Development Practice Question
A machine learning team is using MLflow on Databricks to manage experiments. They want to ensure that their model training runs are reproducible and that they can compare different runs effectively. Which TWO practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is overlooking that logging both parameters and metrics is necessary; one without the other limits reproducibility and 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
✓
Log the evaluation metrics for each run using `mlflow.log_metrics()`.
Logging hyperparameters and evaluation metrics are fundamental for reproducibility and comparison. Hyperparameters record the configuration, while metrics provide quantitative performance. Together, they enable filtering, sorting, and selecting the best runs. Other practices like using a single run, storing full datasets, or date-based naming do not support effective experiment tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the experiment name to the current date to avoid confusion.
Why it's wrong here
Naming experiments by date may help organize runs chronologically, but it does not contribute to reproducibility or comparison. Experiment names should be descriptive of the project or model. Date-based names can become ambiguous and do not capture the purpose. The key practices for reproducibility are logging parameters and metrics, not naming conventions.
- ✗
Store the training dataset in the MLflow run's artifact repository for every run.
Why it's wrong here
Storing the full dataset as an artifact for every run is inefficient and often impractical due to size. MLflow is designed to track metadata and models, not large datasets. Instead, log a reference to the data (e.g., a Delta table version or path) to maintain reproducibility without duplicating storage. This practice avoids bloating the artifact store and keeps runs lightweight.
- ✗
Use a single MLflow run for all experiments to simplify tracking.
Why it's wrong here
Using a single run for multiple experiments mixes parameters and metrics, making it impossible to attribute results to specific configurations. MLflow runs are designed to represent individual executions. Combining them defeats the purpose of tracking and comparison. Each experiment should have its own run to maintain isolation and enable effective analysis.
- ✓
Log the evaluation metrics for each run using `mlflow.log_metrics()`.
Why this is correct
Logging metrics allows quantitative comparison across runs. mlflow.log_metrics() records metrics such as accuracy, loss, or AUC at each step or epoch. This data is used in the MLflow UI to visualize performance, select the best run, and detect overfitting. Without metrics, you cannot objectively evaluate or compare models, making it a fundamental practice for experiment tracking.
- ✓
Log all hyperparameters used in each run using `mlflow.log_params()`.
Why this is correct
Logging hyperparameters is essential for reproducibility and comparison. Without them, you cannot know what configuration produced a given metric. mlflow.log_params() records them in the run, allowing you to filter, sort, and compare runs in the MLflow UI. This practice ensures that the exact settings can be recreated, which is a core requirement for reproducible machine learning experiments.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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