Exam AWS Certified Machine Learning - Specialty topic 1 question 105 discussion

I would say D, because of correlations and dependencies between features. See https://towardsdatascience.com/basics-of-bayesian-network-79435e11ae7b and https://www.quora.com/Whats-the-difference-between-a-naive-Bayes-classifier-and-a-Bayesian-network?share=1

It should be D. Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.

In this case, the absolute values of the Pearson correlation coefficients range between 0.1 to 0.95. This means that some of the features are statistically dependent. Therefore, a full Bayesian network is a better model for the underlying data than a naive Bayesian model.

In a full Bayesian network, features are connected to each other by edges that represent their conditional dependence relationships. A full Bayesian network is useful when the relationships between the features are complex, non-linear or when they are not conditionally independent. In this situation, where the Pearson correlation coefficients range between 0.1 and 0.95, it suggests that there are dependencies between the features, indicating that a full Bayesian network would be appropriate to capture the relationships between the features and model the data.

distinction between Bayes theorem and Naive Bayes is that Naive Bayes assumes conditional independence where Bayes theorem does not. This means the relationship between all input features are independent . The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables.

A naive Bayesian model, since some of the features, are statistically dependent.

D. Naive bayes - features are independent given the class.

I would say, B. Naive Bayes assumes conditional independence and not statistical

This is also a good source of information to help build your understanding https://www.simplypsychology.org/correlation.html