Databricks Machine Learning Associate Question 50
Select 3A data scientist is using Databricks AutoML to build a machine learning model for predicting customer churn. Which steps in the machine learning workflow are automatically completed by AutoML?
- A
Data preprocessing and feature engineering
- B
Hyperparameter tuning for candidate models
- C
Model deployment to a production environment
- D
Model evaluation using validation metrics
- E
Exploratory data analysis (EDA) and visualization
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks AutoML automates key steps of the machine learning workflow, including data preprocessing, feature engineering, hyperparameter tuning, and model evaluation. However, it does not automate tasks like exploratory data analysis or model deployment, as these often require specific domain knowledge or manual intervention.
- A. Correct.
AutoML in Databricks performs data preprocessing and feature engineering, such as handling missing values, encoding categorical variables, and creating relevant features automatically.
- B. Correct.
AutoML includes hyperparameter tuning to optimize candidate models by automatically testing various configurations and selecting the best-performing model.
- C. Incorrect.
Model deployment is not an automatic step in Databricks AutoML; it must be handled manually after the model is trained and evaluated.
- D. Correct.
AutoML evaluates models using validation metrics, such as accuracy, precision, or F1 score, to determine the effectiveness of the candidate models.
- E. Incorrect.
Exploratory data analysis (EDA) and visualization are not fully automated by Databricks AutoML. These tasks typically require manual input and domain expertise.