Q29 — AWS AIF-C01 Ch.3

Question 29 of 100 | ← Chapter 3

A data scientist observes that a model achieves high accuracy on training data but low accuracy on test data. What phenomenon explains these results?

• A. Insufficient training time
• B. Underfitting
• C. Excessive training data
• D. Overfitting ✓

Correct Answer: D. Overfitting

Explanation

High accuracy on training data but low accuracy on test data is a classic sign of overfitting. Overfitting occurs when a model is overly complex and learns not only meaningful patterns but also noise and incidental details in the training data, impairing its ability to generalize to new, unseen data and resulting in poor test performance.