Which professionals are involved in data preprocessing and training and interpret anomalous outputs?

• A. AI ethicists
• B. Data stewards/owners
• C. Data engineers and scientists
• D. Privacy experts

Answer: (C)

Handling the data preparation, model training, and interpretation of unusual outputs is a practical ML workflow led by data engineers and data scientists. Data engineers build and maintain the data pipelines, clean and transform data, handle missing values, and engineer features so models can learn reliably. Data scientists choose suitable algorithms, train the models on the prepared data, tune parameters, and assess performance, while also examining and interpreting any anomalous or unexpected outputs to diagnose issues. This combination directly covers both the data-side prep and the modeling-and-diagnostics side, making it the best fit for these tasks. Roles like data stewards/owners, privacy experts, or AI ethicists contribute important governance, privacy, and ethical considerations, but they aren’t typically responsible for the hands-on preprocessing, training, and interpretation work.