A company is building an ML model with Amazon SageMaker and needs a centralized way to share and manage feature variables across multiple teams. Which SageMaker capability should they use?

Amazon SageMaker Feature Store is the correct choice because it provides a centralized repository for storing, discovering, and sharing machine learning features. This allows multiple teams to reuse features consistently across different models and projects, ensuring data consistency and reducing redundant feature engineering efforts. SageMaker Data Wrangler is used for data preparation and feature engineering, not for centralized feature management and sharing. SageMaker Clarify helps detect bias and explain model predictions, which is a post-training analysis. SageMaker Model Cards provide documentation for models, but not a repository for features themselves.