A lender is preparing a tabular loan dataset in Amazon S3 for a default-prediction model. The annual_income column is missing for about 6% of rows, the gaps are scattered across all customer segments, and the column is heavily right-skewed: most values fall between $30,000 and $120,000, but a handful of records exceed $20 million. Business analysts will build the cleaning logic in AWS Glue DataBrew as a reusable recipe, without writing code.
Which recipe step is the most appropriate way to handle the missing incomes?