For example, consider a dataset that includes three attributes: ID, age, and height. The ID attribute is a randomly generated or sequential number that carries no signal for the ML problem and was not used when training the ML model. You can now configure your Batch Transform jobs to exclude the ID attribute from each record, and pass only the age and height attributes in the prediction requests sent to the model. You can also configure your Batch Transform jobs to associate the ID attribute with the prediction results in the final S3 output of the job. Retaining record-level attributes in this way can be useful for analyzing the prediction results.