Question

A data scientist launches a Databricks AutoML classification experiment through the UI on a customer churn dataset. Before reviewing model results, they want to understand data quality issues such as missing values, high-cardinality columns, and column correlations that AutoML detected during preprocessing. Which artifact should they open to find this information?

Accepted Answer

The data exploration notebook automatically generated by AutoML at the top of the experiment page

Answer

The MLflow run artifacts of the best-performing model

Answer

The generated notebook for the best trial, in its model training cells

Answer

The experiment's 'Schema' tab in the Model Registry entry