Databricks Machine Learning Associate Question 46
Select 4You are using Databricks AutoML to train a machine learning model on a dataset. Which of the following steps in the machine learning workflow are automatically completed by Databricks AutoML?
- A
Data cleaning and preprocessing, including handling missing values
- B
Feature engineering, including automatic selection of features
- C
Hyperparameter tuning for multiple models
- D
Model deployment to a production environment
- E
Exploratory Data Analysis (EDA), including visualization of data distributions
- F
Generating a final production-ready pipeline
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Databricks AutoML automates several key steps in the machine learning workflow, including data preprocessing, feature engineering, hyperparameter tuning, and providing EDA insights. However, it does not handle model deployment or create a fully production-ready pipeline, which remain the responsibility of the user or additional tools.
- A. Correct.
Databricks AutoML performs data cleaning and preprocessing, such as handling missing values, as part of its automated workflow.
- B. Correct.
AutoML includes feature engineering steps, such as selecting important features automatically, to optimize model performance.
- C. Correct.
Hyperparameter tuning is a key part of AutoML, where multiple models are trained and tuned to determine the best-performing configuration.
- D. Incorrect.
Model deployment is not handled by Databricks AutoML; this step requires manual intervention or the use of additional tools.
- E. Correct.
Databricks AutoML provides visualizations and insights during the Exploratory Data Analysis (EDA) phase to help users understand the data.
- F. Incorrect.
AutoML does not automatically generate a final production-ready pipeline; users need to further refine the outputs or integrate them into their production setup.