An ML engineer receives datasets containing missing values, duplicates, and extreme outliers and must merge them into a single dataframe and prepare the data for machine learning. Which solution meets these needs?

Amazon SageMaker Data Wrangler is the correct choice because it is specifically designed for data preparation and feature engineering. It allows users to import and merge various data sources into a single dataframe and provides a visual interface with built-in transformations for handling missing values, duplicates, and outliers, directly addressing the problem statement's requirements. Amazon SageMaker Ground Truth is for data labeling, not general data preparation or merging. Manually importing and merging data followed by using Amazon Q Developer might generate code, but Data Wrangler offers a more integrated and efficient solution for the specified tasks. Amazon SageMaker data labeling is also for labeling, not data cleansing or merging.