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…

The correct answer is D. The model is overfit. Overfitting occurs when a model learns the training data too well, including noise and details that do not generalize. As a result, it performs well on training data but poorly on unseen evaluation

Submitted by suresh_in· Mar 30, 2026

Question

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?

Options

AThe model is underfit.
BThe model requires prompt engineering.
CThe model is biased.
DThe model is overfit.

How the community answered

(28 responses)

A
4% (1)
B
7% (2)
C
14% (4)
D
75% (21)

Explanation

Overfitting occurs when a model learns the training data too well, including noise and details that do not generalize. As a result, it performs well on training data but poorly on unseen evaluation

Topics

#Overfitting#Model evaluation#Training data#Evaluation data

Community Discussion

No community discussion yet for this question.

Full AIF-C01 Practice