A data scientist trains a classification model to predict whether an email is spam or not. The model achieves 98% accuracy on the test set, but upon inspection, it classifies all emails as 'not spam' because the dataset has 95% non-spam emails. What is the most likely issue?

Overfitting

Overfitting occurs when a model learns training data too well and fails to generalize, typically resulting in a large gap between training and test accuracy. Here the accuracy is suspiciously high on the test set, but the model is trivial, not overfitted.

Underfitting

Underfitting happens when a model is too simple to capture patterns, leading to low accuracy on both training and test sets. That does not match the 98% accuracy.

Feature scaling error

Feature scaling issues can affect model convergence or performance, but they do not cause a model to output a constant class prediction unless combined with other factors.

What does this AI-900 question test?

Static NAT maps one inside address to one outside address.

What is the correct answer to this question?

The correct answer is: Data imbalance — This scenario describes class imbalance, where the majority class (non-spam) dominates the dataset. The model achieves high accuracy by simply predicting the majority class for every instance, which is not useful. Overfitting would show high training accuracy but lower test accuracy; underfitting would yield low accuracy on both sets; and feature scaling error is unrelated to the described behavior.

What should I do if I get this AI-900 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.