- A
Linear regression
Correct: Linear regression models the relationship between dependent and independent variables for continuous output.
- B
K-means
Why wrong: K-means is unsupervised clustering, not prediction.
- C
Decision tree
Why wrong: Decision tree can perform regression, but linear regression is more appropriate for monthly sales trend.
- D
Logistic regression
Why wrong: Logistic regression is for classification, not continuous prediction.
Question 923 of 1,000
Linear Regression for Sales Prediction — Algorithm Selection | CompTIA AI+ Explained
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 team wants to predict monthly sales using historical data. Which algorithm is most appropriate?
Quick Answer
The answer is linear regression, as it is the most appropriate algorithm for predicting monthly sales from historical data. Linear regression models the relationship between input features and a continuous target variable by fitting a straight line to minimize prediction error, making it ideal for forecasting numerical outcomes like sales figures. On the CompTIA AI+ AI0-001 exam, this question tests your ability to match algorithms to problem types—specifically distinguishing regression from classification and clustering tasks. A common trap is confusing linear regression with logistic regression, which is used for binary outcomes, or assuming decision trees are always better for trends, though linear regression offers simpler interpretability for steady patterns. For a quick memory tip: think “linear for lines, logistic for labels, K-means for groups.”
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 appropriate algorithm because the goal is to predict a continuous numerical value (monthly sales) based on historical data. It models the relationship between input features and the target variable by fitting a linear equation, making it ideal for regression tasks where the output is a real number.
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.
- ✓
Linear regression
Why this is correct
Correct: Linear regression models the relationship between dependent and independent variables for continuous output.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
K-means
Why it's wrong here
K-means is unsupervised clustering, not prediction.
- ✗
Decision tree
Why it's wrong here
Decision tree can perform regression, but linear regression is more appropriate for monthly sales trend.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is for classification, not continuous prediction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between regression and classification algorithms, and the trap here is that candidates may confuse 'regression' in logistic regression with continuous prediction, not realizing it is actually a classification algorithm.
Detailed technical explanation
How to think about this question
Linear regression works by minimizing the sum of squared residuals between observed and predicted values using the ordinary least squares (OLS) method. Under the hood, it assumes a linear relationship between independent variables and the target, and it requires that features are not highly correlated (multicollinearity) to produce stable coefficient estimates. In a real-world scenario, a retail company might use linear regression to forecast monthly sales based on factors like advertising spend and seasonality, but must check for homoscedasticity and normality of residuals to ensure valid inference.
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 appropriate algorithm because the goal is to predict a continuous numerical value (monthly sales) based on historical data. It models the relationship between input features and the target variable by fitting a linear equation, making it ideal for regression tasks where the output is a real number.
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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