- A
K-means clustering
Why wrong: Clustering is unsupervised and used for grouping, not prediction of a continuous value.
- B
Decision tree regression
Why wrong: Decision trees can predict continuous values, but linear regression is often more suitable and interpretable for simple linear relationships.
- C
Association rule mining
Why wrong: Association rules find relationships between items in transactions, not for regression.
- D
Linear regression
Linear regression models the linear relationship between input features and a continuous output.
Question 541 of 1,000
Linear Regression for Housing Price Prediction
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 analyst wants to predict housing prices based on square footage, number of bedrooms, and location. Which machine learning approach is most suitable?
Quick Answer
The answer is linear regression, as it is the most suitable machine learning approach for regression tasks involving a continuous target variable like housing price. This model works by establishing a linear relationship between the input features—square footage, number of bedrooms, and location—and the output price, making it both simple to implement and highly interpretable. On the CompTIA AI+ AI0-001 exam, this question tests your ability to match problem types with appropriate algorithms; a common trap is confusing regression with classification, where the goal is to predict categories rather than numeric values. Remember, if the output is a number (like price), you are in regression territory, and linear regression is the go-to baseline. For a quick memory tip: think “continuous output = linear regression,” and visualize a straight line fitting scattered data points on a graph.
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
✓
Linear regression
Linear regression is the most suitable approach because the problem involves predicting a continuous numeric target (housing prices) from multiple independent variables (square footage, bedrooms, location). Linear regression models the linear relationship between the features and the target, providing interpretable coefficients and efficient training for this type of regression task.
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.
- ✗
K-means clustering
Why it's wrong here
Clustering is unsupervised and used for grouping, not prediction of a continuous value.
- ✗
Decision tree regression
Why it's wrong here
Decision trees can predict continuous values, but linear regression is often more suitable and interpretable for simple linear relationships.
- ✗
Association rule mining
Why it's wrong here
Association rules find relationships between items in transactions, not for regression.
- ✓
Linear regression
Why this is correct
Linear regression models the linear relationship between input features and a continuous output.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse regression (predicting a continuous value) with classification or unsupervised learning, and incorrectly select decision tree regression or clustering because they see 'prediction' and assume any tree-based or grouping method works.
Detailed technical explanation
How to think about this question
Linear regression assumes a linear relationship between the independent variables and the dependent variable, minimizing the sum of squared residuals via ordinary least squares (OLS). In practice, location would need to be encoded (e.g., one-hot encoding) to be used as a categorical feature. A real-world scenario where this matters is in real estate valuation, where a baseline linear model often serves as a benchmark before exploring more complex models.
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.
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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: Linear regression — Linear regression is the most suitable approach because the problem involves predicting a continuous numeric target (housing prices) from multiple independent variables (square footage, bedrooms, location). Linear regression models the linear relationship between the features and the target, providing interpretable coefficients and efficient training for this type of regression task.
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
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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