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
