The correct answer is B. Creating a batch prediction job using the actual sales data and configuring the job settings to generate feature attributions provides insights into how each feature in the data contributes to the model's predictions. This approach allows you to directly demonstrate how the model uses different features to make predictions, making it easier to explain the model's reasoning to stakeholders. Comparatively, simply comparing predictions to actuals (Option A) does not explain how the model arrives at those predictions. Generating counterfactual examples (Option C) can be useful for understanding model behavior but is not necessary for explaining the basic prediction process. Training another model (Option D) is unnecessary and time-consuming when feature attributions can provide the needed insights without additional model training.