The correct answer is A. The XRAI method is designed for interpreting image classification models and is not suitable for this use case, where we are dealing with tabular data for loan approval analysis. The sampled Shapley method is well-suited for explaining complex models like neural networks and does not require retraining the entire model. Since monitoring performance over time is crucial, checking for feature distribution drift is appropriate because drift indicates a change in the underlying data distribution, which can impact model performance. Skew is less relevant in this context since it refers to differences between training and serving feature distributions, rather than temporal changes in the data.