Question

A data analyst wants to quickly benchmark several models for a customer churn classification problem using the Databricks AutoML UI. They have a Delta table registered in Unity Catalog and want AutoML to automatically try multiple algorithms and generate reproducible notebooks. When configuring the AutoML experiment through the UI, which combination of settings must they specify for the experiment to run correctly?

Accepted Answer

The ML problem type (Classification), the input training dataset, and the prediction target column

Answer

The prediction target column, a pre-trained baseline model, and a manually written evaluation notebook

Answer

The ML problem type (Classification), the MLflow Model Registry alias, and the number of Spark executors

Answer

A feature table lookup key, the primary metric only, and a published online store endpoint