A company wants to build an ML model to predict customer satisfaction and requires fully automated model tuning. Which AWS service should they use?

Amazon SageMaker is the correct choice because it offers comprehensive machine learning capabilities, including automated model tuning through SageMaker Automatic Model Tuning (hyperparameter optimization). This feature allows users to find the best version of a model by running many training jobs with different hyperparameter combinations, which directly addresses the requirement for fully automated model tuning. Amazon Personalize is a recommendation service, not a general-purpose ML platform for custom model building and tuning. Amazon Athena is a query service for S3 data, not an ML service. Amazon Comprehend is a natural language processing (NLP) service that provides pre-trained models for text analysis, but it doesn't offer custom model building or automated tuning for arbitrary ML models.