- A
Good fit
Why wrong: Good fit has low bias and low variance.
- B
Data leakage
Why wrong: Data leakage is unrelated to bias-variance tradeoff.
- C
Overfitting
Why wrong: Overfitting has low bias and high variance.
- D
Underfitting
Correct: High bias leads to underfitting.
Question 887 of 1,000
Bias-Variance Tradeoff: Diagnosing Underfitting
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A model trained on a dataset has high bias and low variance. What does this indicate?
Quick Answer
The answer is underfitting. High bias and low variance indicate that the model is too simplistic, failing to capture the underlying patterns in the data, which is the hallmark of underfitting. This occurs because the model makes strong assumptions about the data (high bias) while remaining insensitive to variations across different training samples (low variance). On the CompTIA AI+ AI0-001 exam, this concept tests your ability to diagnose model performance using the bias-variance tradeoff. A common trap is confusing underfitting with overfitting, which instead shows low bias and high variance. A useful memory tip is to think of a rigid ruler: it has high bias (always straight) and low variance (consistent), but it cannot bend to fit the curve of the data—just like an underfit model.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Good fit has low bias and low variance.
- ✗
Data leakage
Why it's wrong here
Data leakage is unrelated to bias-variance tradeoff.
- ✗
Overfitting
Why it's wrong here
Overfitting has low bias and high variance.
- ✓
Underfitting
Why this is correct
Correct: High bias leads to underfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, bias measures the error introduced by approximating a real-world problem with a simplified model, while variance measures the model's sensitivity to fluctuations in the training data. In practice, underfitting often occurs when using a linear model on non-linear data, or when regularization strength (e.g., lambda in ridge regression) is set too high, forcing the model toward a constant prediction.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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.
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