AI0-001 Machine Learning and Deep Learning Practice Question
A model trained on a dataset has high bias and low variance. What does this indicate?
⚠ Common exam trap
The CompTIA AI exam often tests the bias-variance tradeoff by reversing the definitions, so candidates mistakenly associate high bias with overfitting or high variance with underfitting.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Underfitting
High bias and low variance indicate that the model is too simple to capture the underlying patterns in the data, leading to systematic errors on both training and test sets. This is the classic signature of underfitting, where the model fails to learn the training data adequately.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Good fit
Why it's wrong here
A good fit requires low bias and low variance; high bias with low variance is underfitting, where the model is too simple to capture the underlying pattern. It is tempting because low variance alone sounds desirable, and would be correct if both bias and variance were simultaneously low.
- ✗
Data leakage
Why it's wrong here
Data leakage inflates accuracy by exposing training data to test information; it does not produce high bias with low variance. It is tempting because leakage is a common cause of misleadingly strong results, and would be the answer if a model scored unrealistically well in evaluation but poorly in production.
- ✗
Overfitting
Why it's wrong here
High bias with low variance describes underfitting: the model is too simple to capture the underlying pattern, so training and validation errors are both high and close. Overfitting is the opposite — low bias, high variance. Overfitting would be the answer when training error is low but validation error is markedly higher.
- ✓
Underfitting
Why this is correct
High bias means the model is too simple to capture the underlying pattern, and low variance means its predictions stay consistently wrong across samples. Together these signal underfitting, where training and validation errors both remain high.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.