According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe core concepts of machine learning on Azure”, when building a predictive machine learning model, features are the input variables used by the algorithm to predict the target label. The target label is the output or value the model is trained to predict.

In this dataset, the target variable is clearly the Income Range, since the goal is to predict a customer’s income bracket. Therefore, Income Range (D) is the label, not a feature. Features must be other attributes that help the model make this prediction.

The fields Education Level (A) and Age (C) are the most relevant features because both can logically and statistically influence income level.

Education Level is a categorical variable that often correlates strongly with income. Individuals with higher education levels tend to earn more on average, making this an important predictor.

Age is a numerical variable that typically affects income level due to factors such as experience and career progression.

By contrast:

First Name (E) and Last Name (B) are irrelevant as features because they are identifiers, not meaningful predictors of income. Including them could lead to bias or model overfitting without contributing to accurate predictions.

Hence, according to AI-900 principles, the features used to train a model predicting income range would be Education Level and Age.