The verified answer is B. Amazon SageMaker Clarify. The model is used for customer loan applications, where explainability, transparency, and human-centered decision support are critical. AWS documentation states that SageMaker Clarify provides tools to help explain how machine learning models make predictions. These tools help ML developers, modelers, and internal stakeholders understand model characteristics before deployment and debug predictions after deployment.

SageMaker Clarify is specifically aligned with explainable AI because it provides feature attribution methods, including SHAP-based explanations. AWS documentation explains that Clarify can generate reports that contribute to model governance documentation and can help describe how a model works, what features influence its decisions, and how explanations are generated.

Amazon SageMaker Model Registry is incorrect because it manages model versions, approval status, and deployment tracking. It supports governance workflows, but it does not explain why a model made a decision.

Amazon SageMaker Pipelines is incorrect because it automates and orchestrates ML workflows such as training, evaluation, and deployment. It does not provide the core explainability function required here.

Amazon SageMaker Feature Store is incorrect because it stores, shares, and serves ML features for training and inference. It improves feature reuse and consistency, but it does not explain model decisions.

For a financial loan application model, stakeholders and regulators need to understand which factors influenced predictions. SageMaker Clarify is the AWS feature designed for this explainability requirement, so option B is correct.