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A data science team is using AWS Glue DataBrew to prepare and clean a large dataset stored in Amazon S3 for a machine learning project. The dataset must be prepared periodically based on incoming data triggers. This preparation includes transformation, normalization, and column manipulations. After the dataset is prepared, data quality metrics must be sent to Amazon CloudWatch to monitor the data preparation process. Which approach correctly implements these requirements?