Databricks Machine Learning Associate Question 56
Select 3A data scientist is using Databricks AutoML to create a machine learning model for predicting customer churn. After running the AutoML experiment, they observe multiple models in the experiment dashboard with different metrics. Which of the following statements are true about using Databricks AutoML in this scenario?
- A
Databricks AutoML automatically splits the dataset into training, validation, and test sets during the experiment.
- B
The user must manually select the final model from the AutoML experiment results based on the desired evaluation metric.
- C
Databricks AutoML provides feature importance visualizations for the generated models.
- D
Databricks AutoML supports both regression and classification problems, but not time series forecasting.
- E
Hyperparameter tuning is handled automatically by Databricks AutoML during the model training process.
Show answer and explanation
Correct answers: A, C, E
Explanation
Databricks AutoML simplifies the machine learning workflow by automating key steps such as data splitting, hyperparameter tuning, and model evaluation. It also provides visual insights like feature importance and supports a variety of tasks, including regression, classification, and time series forecasting. Users can review the experiment dashboard to select or validate the most suitable model for their requirements.
- A. Correct.
This is correct. Databricks AutoML automatically splits the dataset into training, validation, and test sets to ensure proper evaluation of the models.
- B. Incorrect.
This is incorrect. While the user can choose the final model manually, Databricks AutoML highlights the best-performing model based on the primary evaluation metric automatically.
- C. Correct.
This is correct. Databricks AutoML provides feature importance visualizations to help users understand which features contribute most to the model predictions.
- D. Incorrect.
This is incorrect. Databricks AutoML supports regression, classification, and time series forecasting tasks.
- E. Correct.
This is correct. Databricks AutoML automatically handles hyperparameter tuning to optimize the models during the training process.