A dataset contains ordered tabular features with sensitive values that must be masked (not discarded) before another team begins model development. Which service should the ML engineer use to mask the sensitive fields with minimal effort?

AWS Glue DataBrew is the most suitable service because it is a visual data preparation tool that allows users to clean and normalize data without writing code. It offers built-in transformations, including various masking and anonymization options for sensitive data, which directly addresses the requirement to mask sensitive fields with minimal effort. Amazon Macie is primarily a data security and privacy service that uses machine learning to discover, classify, and protect sensitive data. While it can identify sensitive data, it does not directly offer data transformation or masking capabilities for preparing data for model development. Running an AWS Batch job or an Amazon EMR job to replace sensitive values would require custom code development (e.g., Python, Spark), which goes against the "minimal effort" requirement. These services are powerful for large-scale processing but are not designed for no-code data preparation and masking like DataBrew.