- A
L1 regularisation (Lasso)
Why wrong: L1 can zero out coefficients, which may be too aggressive if all features are relevant; L2 is generally safer for reducing variance without losing features.
- B
Dropout
Why wrong: Dropout is used in neural networks, not linear regression.
- C
L2 regularisation (Ridge)
Ridge adds squared magnitude penalty, shrinking coefficients smoothly, which helps generalise.
- D
Data augmentation
Why wrong: Data augmentation increases training data, but the question asks for a regularisation technique.
Question 142 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is using a linear regression model to predict house prices and observes that the model performs well on training data but poorly on test data. Which regularisation technique is MOST appropriate to reduce overfitting?
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
✓
L2 regularisation (Ridge)
L2 regularisation (Ridge) adds a penalty term equal to the sum of the squared coefficients to the loss function, which shrinks coefficient magnitudes without forcing them to zero. This reduces variance and overfitting by making the model less sensitive to individual features, which is ideal when the model performs well on training data but poorly on test data due to high variance.
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.
- ✗
L1 regularisation (Lasso)
Why it's wrong here
L1 can zero out coefficients, which may be too aggressive if all features are relevant; L2 is generally safer for reducing variance without losing features.
- ✗
Dropout
Why it's wrong here
Dropout is used in neural networks, not linear regression.
- ✓
L2 regularisation (Ridge)
Why this is correct
Ridge adds squared magnitude penalty, shrinking coefficients smoothly, which helps generalise.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data augmentation
Why it's wrong here
Data augmentation increases training data, but the question asks for a regularisation technique.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between L1 and L2 regularisation by presenting a scenario where feature selection is not needed, and candidates mistakenly choose Lasso because they confuse 'reducing coefficients' with 'eliminating coefficients'.
Detailed technical explanation
How to think about this question
L2 regularisation modifies the ordinary least squares objective function to minimize (RSS + λ * Σβ_j²), where λ controls the strength of regularisation. A subtle behavior is that L2 regularisation assumes all features are equally important and shrinks them proportionally, which works well when multicollinearity is present. In real-world scenarios like predicting house prices with many correlated features (e.g., square footage and number of bedrooms), Ridge regression stabilises coefficient estimates and improves test set generalisation.
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?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: L2 regularisation (Ridge) — L2 regularisation (Ridge) adds a penalty term equal to the sum of the squared coefficients to the loss function, which shrinks coefficient magnitudes without forcing them to zero. This reduces variance and overfitting by making the model less sensitive to individual features, which is ideal when the model performs well on training data but poorly on test data due to high variance.
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
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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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