Q44 — AWS AIF-C01 Ch.1

Question 44 of 100 | ← Chapter 1

A company wants to classify human genomic data into 20 categories based on genetic features. The company requires an ML algorithm whose internal mechanisms are interpretable to understand how they influence output results. Which ML algorithm meets these requirements?

• A. Decision tree ✓
• B. Linear regression
• C. Logistic regression
• D. Neural network

Correct Answer: A. Decision tree

Explanation

This question tests knowledge of characteristics of different ML algorithms. Decision trees provide clear, visualizable decision rules and internal logic, making them highly interpretable for understanding how inputs affect outputs. Linear regression and logistic regression are primarily used for continuous-value prediction and binary classification, respectively, and lack granular interpretability for multi-class genomic classification. Neural networks are complex and inherently opaque ('black-box'), making it difficult to trace how internal mechanisms influence outputs. Therefore, the decision tree algorithm best satisfies the requirement for interpretability.