AWS Certified AI Practitioner (AIF-C01) — Question 336

An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data.

What is the MOST likely cause of this issue?

Answer options

• A. The model is underfit.
• B. The model requires prompt engineering.
• C. The model is biased.
• D. The model is overfit.

Correct answer: D

Explanation

Overfitting occurs when a model learns the training data too well, including its noise and outliers, which prevents it from generalizing effectively to new evaluation data. Underfitting would cause poor performance on both the training and evaluation datasets. Bias refers to systematic errors in assumptions, and prompt engineering is a technique for interacting with large language models rather than a cause of generalization failure.