A data engineer must load a dataset into an Amazon S3 data lake using AWS services. After profiling the data, the engineer discovers personally identifiable information (PII). The engineer needs to both profile the dataset and obfuscate the PII. Which approach will satisfy this requirement with the LEAST operational effort?

The correct answer leverages AWS Glue Studio's built-in "Detect PII" transform, which simplifies identifying PII without custom code, minimizing operational effort. After identification, the PII can be obfuscated directly within Glue Studio or with a subsequent Glue transform. AWS Step Functions then orchestrates the entire data pipeline, including ingestion into S3, providing a managed and scalable solution. Incorrect options: Kinesis Data Firehose with a Lambda transform requires custom code for PII detection and obfuscation, increasing operational overhead compared to Glue Studio's managed transform. While AWS Glue Data Quality can detect data quality issues, it's not designed for direct PII obfuscation as a primary function; custom logic would still be needed. Ingesting into DynamoDB first adds an unnecessary intermediate step and cost, and requires custom Lambda functions for both PII handling and S3 ingestion, increasing complexity.