You are an ML engineer at a bank. You need to build a solution that provides transparent and understandable explanations for AI-driven decisions for loan approvals, credit limits, and interest rates. You want to build this system to require minimal operational overhead. What should you do?

A. Deploy the Learning Interpretability Tool (LIT) on App Engine to provide explainability and visualization of the output. B. Use Vertex Explainable AI to generate feature attributions, and use feature-based explanations for your models. C. Use AutoML Tables with built-in explainability features, and use Shapley values for explainability. D. Deploy pre-trained models from TensorFlow Hub to provide explainability using visualization tools.