Question

A data engineer is preparing a customer dataset for a churn prediction model. The 'subscription_tier' column contains inconsistent values entered by different systems, such as 'Premium', 'premium', 'PREMIUM ', and 'Prem.'. These should all represent the same category before one-hot encoding. The engineer wants a low-code, visual approach to detect and consolidate these variants without writing custom Spark code. Which AWS Glue DataBrew capability best addresses this requirement?

Accepted Answer

Apply a recipe combining a lowercase transform, whitespace trimming, and a categorical mapping (replace) step to standardize the variant values to a single canonical value.

Answer

Use the DataBrew outlier detection transform to remove the inconsistent categorical values from the dataset.

Answer

Run a Glue FindMatches ML transform to deduplicate the rows before encoding.

Answer

Apply quantile binning to group the categorical values into numeric buckets.