- A
Overfitting
Correct: low training error and high test error is classic overfitting.
- B
Multicollinearity
Why wrong: Multicollinearity does not directly cause a large gap between training and test error.
- C
Data leakage
Why wrong: Data leakage would cause both training and test error to be low, not just training.
- D
Underfitting
Why wrong: Underfitting would show high training error, not low.
Question 169 of 1,000
Linear Regression Overfitting — Diagnosis and Solutions | CompTIA AI+ Explained
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.
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.”
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
✓
Overfitting
Low training error combined with high test error is the classic symptom of overfitting, where the model has memorized the training data, including its noise, rather than learning the underlying patterns. This causes the model to perform poorly on unseen data, which is exactly what the high test error indicates.
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.
- ✓
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
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between overfitting and underfitting by presenting a scenario where training error is low but test error is high, leading candidates to mistakenly choose underfitting because they focus only on the high test error without considering the low training error.
Trap categories for this question
Command / output trap
Underfitting would show high training error, not low.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model has too many parameters relative to the number of training observations, allowing it to fit the training data's idiosyncrasies. Techniques like regularization (L1/L2), cross-validation, and pruning are used to mitigate overfitting by penalizing model complexity or ensuring the model generalizes. In real-world scenarios, overfitting is common when using high-degree polynomial features or deep neural networks without sufficient data or regularization.
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: Overfitting — Low training error combined with high test error is the classic symptom of overfitting, where the model has memorized the training data, including its noise, rather than learning the underlying patterns. This causes the model to perform poorly on unseen data, which is exactly what the high test error indicates.
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
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