The verified answer is A. Present the model Shapley values. AWS documentation states that SageMaker Clarify provides feature attributions based on Shapley values. These values can determine the contribution each feature made to model predictions and can be provided for individual predictions or at a global level for the model as a whole. That is exactly what stakeholders need when they ask for transparency and explainability: they need to understand which features influenced the model’s decisions and how strongly those features contributed.
AWS also explains that SageMaker Clarify helps explain how ML models make predictions and that transparency is critical for consumers and regulators. Clarify’s explainability uses a model-agnostic feature attribution approach and includes a scalable implementation of SHAP. These explanations answer practical stakeholder questions such as why the model made a prediction, how it makes predictions, and which features have the largest influence on model behavior.
Option B is incorrect because accuracy measures predictive correctness, not explainability. A model can be highly accurate while still being opaque. Accuracy alone does not show why a specific prediction was made.
Option C is incorrect because a confusion matrix summarizes classification performance by showing true positives, false positives, true negatives, and false negatives. It is useful for evaluation, but it does not explain individual feature influence or model reasoning.
Option D is incorrect because a secure inference endpoint protects access to the deployed model. Security is important, but it does not provide transparency or explainability.
Therefore, presenting Shapley values is the correct solution because it directly explains feature contributions to model predictions.
