Quick Answer
The correct answer is overfitting, because the model has memorized the training data’s noise rather than learning the underlying pattern, resulting in low training error but high test error. This is the classic signature of overfitting in linear regression: the model becomes too complex, capturing random fluctuations that don’t generalize to new data. On the CompTIA AI+ AI0-001 exam, this scenario tests your ability to distinguish overfitting from underfitting—a common trap is confusing it with data leakage, but leakage would inflate both training and test performance. Remember, if training error is low and test error is high, think “overfit, not underfit.” A quick memory tip: “Low train, high test? The model’s a memorizing pest.”
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 data scientist trains a linear regression model on housing prices. The training error is low, but test error is high. What is the most likely issue?
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.
Overfitting
Correct: low training error and high test error is classic overfitting.
Multicollinearity
Why wrong: Multicollinearity does not directly cause a large gap between training and test error.
Data leakage
Why wrong: Data leakage would cause both training and test error to be low, not just training.
Underfitting
Why wrong: Underfitting would show high training error, not low.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Overfitting
Option A is correct because overfitting occurs when the model fits the training data too well but performs poorly on unseen data. Options B, C, and D are incorrect because underfitting would show high training error, data leakage would cause artificially high performance, and multicollinearity affects coefficient interpretation but not necessarily test error.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Overfitting
Why this is correct
Correct: low training error and high test error is classic overfitting.
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.
Multicollinearity
Why it's wrong here
Multicollinearity does not directly cause a large gap between training and test error.
Data leakage
Why it's wrong here
Data leakage would cause both training and test error to be low, not just training.
Underfitting
Why it's wrong here
Underfitting would show high training error, not low.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Underfitting would show high training error, not low.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning and Deep Learning — study guide chapter
Learn the concepts, then practise the questions
Machine Learning and Deep Learning practice questions
Targeted practice on this topic area only
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FAQ
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Overfitting — Option A is correct because overfitting occurs when the model fits the training data too well but performs poorly on unseen data. Options B, C, and D are incorrect because underfitting would show high training error, data leakage would cause artificially high performance, and multicollinearity affects coefficient interpretation but not necessarily test error.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 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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