Comprehensive and Detailed Explanation (AWS AI documents):

Overfitting occurs when an ML model learns patterns too closely from training data , including noise, and fails to generalize to unseen data . According to AWS ML fundamentals, a key indicator of overfitting is high training accuracy combined with reduced accuracy on new or test data .

Option A directly reflects this behavior: the model performs well during training but loses accuracy when evaluated on new data , which is a textbook symptom of overfitting.

Why the other options are not overfitting:

B describes an under-featured model (underfitting risk).

C indicates poor data representativeness, not overfitting.

D reflects limited feature selection, which may lead to underfitting or bias, not overfitting.

AWS AI Study Guide References:

AWS Machine Learning concepts: bias vs. variance

AWS training and evaluation best practices