- A
Normality
Why wrong: Normality concerns the distribution shape, not variance consistency.
- B
Homoscedasticity
Homoscedasticity requires constant variance of residuals; increasing variance violates it.
- C
Linearity
Why wrong: Linearity assumption is about the functional form, not residual variance.
- D
Independence
Why wrong: Independence refers to errors being uncorrelated, not variance pattern.
Question 215 of 1,000
Linear Regression Assumptions — Heteroscedasticity Violation | CompTIA AI+ Explained
This AI0-001 practice question tests your understanding of machine learning and deep learning. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 company uses linear regression to predict sales based on advertising spend. The model's residuals show a pattern of increasing variance as spend increases. Which assumption of linear regression is violated?
Quick Answer
The correct answer is homoscedasticity, as the violation described—increasing residual variance with higher advertising spend—is the textbook definition of heteroscedasticity. In linear regression, homoscedasticity requires that the residuals have constant variance across all levels of the independent variable; when this assumption is broken, the model’s predictions become unreliable, especially at extreme values. On the CompTIA AI+ AI0-001 exam, this concept tests your ability to distinguish between the four core regression assumptions: linearity, independence, normality, and homoscedasticity. A common trap is confusing heteroscedasticity with non-linearity, but remember that linearity concerns the shape of the relationship, not the spread of errors. To lock in the concept, use the mnemonic “Homo means same, Hetero means different”—if the residual spread changes, you’ve violated homoscedasticity.
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
✓
Homoscedasticity
The pattern of increasing residual variance with higher advertising spend violates the assumption of homoscedasticity, which requires constant variance of errors across all levels of the independent variable. In linear regression, heteroscedasticity like this can lead to inefficient coefficient estimates and unreliable confidence intervals, often detected via a Breusch-Pagan test or residual plot analysis.
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.
- ✗
Normality
Why it's wrong here
Normality concerns the distribution shape, not variance consistency.
- ✓
Homoscedasticity
Why this is correct
Homoscedasticity requires constant variance of residuals; increasing variance violates it.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Linearity
Why it's wrong here
Linearity assumption is about the functional form, not residual variance.
- ✗
Independence
Why it's wrong here
Independence refers to errors being uncorrelated, not variance pattern.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA AI exams often test the distinction between homoscedasticity and normality, trapping candidates who confuse residual variance patterns with residual distribution shape, especially when the question describes a 'fan' or 'cone' shape in the residual plot.
Detailed technical explanation
How to think about this question
Heteroscedasticity inflates the standard errors of regression coefficients, making hypothesis tests (e.g., t-tests for slope significance) unreliable. In practice, robust standard errors (e.g., Huber-White sandwich estimators) can correct for this, or a weighted least squares approach can be used if the variance structure is known. Real-world example: sales data often exhibits heteroscedasticity because higher spend introduces more variability in customer response, such as diminishing returns or market saturation effects.
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
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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: Homoscedasticity — The pattern of increasing residual variance with higher advertising spend violates the assumption of homoscedasticity, which requires constant variance of errors across all levels of the independent variable. In linear regression, heteroscedasticity like this can lead to inefficient coefficient estimates and unreliable confidence intervals, often detected via a Breusch-Pagan test or residual plot analysis.
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.
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Last reviewed: Jul 4, 2026
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