Amazon Exam MLS-C01 Topic 1 Question 123 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 123
Topic #: 1

[Exploratory Data Analysis]

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

AA naive Bayesian model, since the features are all conditionally independent.

BA full Bayesian network, since the features are all conditionally independent.

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

DA full Bayesian network, since some of the features are statistically dependent.

Show Suggested Answer

Suggested Answer: D

by Lettie at Oct 30, 2025, 02:09 AM

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Salley

2 hours ago

I'm pretty confident that the answer is D. A full Bayesian network should be used since the features have varying degrees of correlation, indicating they are not all conditionally independent.

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Bobbye

6 days ago

Okay, let me think this through. If some of the features are dependent, then a full Bayesian network would be the better choice to model those relationships. The key is that the question mentions the Pearson correlations, which indicate statistical dependence between the features.

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Rory

11 days ago

I'm a bit confused here. If the features are conditionally independent, then a naive Bayesian model should work, right? But the question says some of the features are dependent, so I'm not sure which model to choose.

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Farrah

17 days ago

Hmm, the Pearson correlation coefficients range from 0.1 to 0.95, so that suggests some of the features are dependent. I think a full Bayesian network might be better to capture those dependencies.

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