- A
Use a more complex model, such as polynomial regression
More complex models have lower bias as they can fit more patterns.
- B
Reduce the number of training samples
Why wrong: Fewer samples typically increase bias and variance.
- C
Add L2 regularization
Why wrong: Regularization increases bias to reduce variance, not decrease bias.
- D
Apply feature scaling
Why wrong: Feature scaling helps convergence but does not directly reduce bias.
Question 872 of 1,000
Reducing High Bias in Linear Regression — Use Polynomial Features
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 machine learning engineer notices that a linear regression model has high bias. Which action is most likely to reduce bias?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The correct answer is to use a more complex model, such as polynomial regression. This directly addresses high bias, which occurs when a model is too simple to capture the underlying patterns in the data, often called underfitting. By adding polynomial features, you increase the model’s flexibility, allowing it to fit nonlinear relationships and thereby reduce bias. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the bias-variance tradeoff—a core concept in model evaluation. A common trap is confusing regularization, which actually increases bias to reduce variance, with complexity adjustments. Remember the memory tip: “Bias is for simplicity; to fix it, add complexity.”
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
✓
Use a more complex model, such as polynomial regression
High bias indicates that the model is too simple to capture the underlying patterns in the data, leading to underfitting. Using a more complex model, such as polynomial regression, increases the model's capacity to fit the training data better, directly addressing the underfitting issue. This is the standard approach to reduce bias in machine learning.
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.
- ✓
Use a more complex model, such as polynomial regression
Why this is correct
More complex models have lower bias as they can fit more patterns.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of training samples
Why it's wrong here
Fewer samples typically increase bias and variance.
- ✗
Add L2 regularization
Why it's wrong here
Regularization increases bias to reduce variance, not decrease bias.
- ✗
Apply feature scaling
Why it's wrong here
Feature scaling helps convergence but does not directly reduce bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the bias-variance tradeoff by making candidates confuse bias-reduction techniques with variance-reduction techniques, such as regularization or reducing training data, which actually increase bias or do not affect it.
Detailed technical explanation
How to think about this question
Bias is the error introduced by approximating a real-world problem with a simplified model. In linear regression, high bias often means the model assumes a linear relationship when the true relationship is nonlinear. Polynomial regression adds higher-degree terms (e.g., x^2, x^3) to the feature set, allowing the model to fit curves and reduce bias. However, increasing model complexity too much can lead to overfitting (high variance), so techniques like cross-validation are used to find the right balance.
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: Use a more complex model, such as polynomial regression — High bias indicates that the model is too simple to capture the underlying patterns in the data, leading to underfitting. Using a more complex model, such as polynomial regression, increases the model's capacity to fit the training data better, directly addressing the underfitting issue. This is the standard approach to reduce bias in machine learning.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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