After aggregating the data, the ML engineer needs an automated way to detect anomalies in the dataset and to visualize the findings. Which solution meets both requirements?

Amazon SageMaker Data Wrangler is designed for data preparation and feature engineering, offering built-in transformations, including anomaly detection capabilities. It also provides integrated visualization tools to help understand the data and the results of transformations, making it a comprehensive solution for both anomaly detection and visualization within a single service. Amazon Athena is a query service and does not inherently offer automated anomaly detection or visualization capabilities. Amazon Redshift Spectrum allows querying data in S3 but doesn't provide anomaly detection. While Amazon QuickSight is excellent for visualization, it's a separate service and doesn't perform anomaly detection itself. AWS Batch is a compute service for running jobs, not a dedicated anomaly detection or visualization tool.