Quick Answer
The answer is decision trees and linear regression. Both are supervised learning algorithms because they learn a mapping from input features to a labeled target variable using training data. Linear regression predicts a continuous outcome by minimizing the difference between predicted and actual values, typically through ordinary least squares, while decision trees split data based on feature thresholds to classify or regress toward a target. On the CompTIA Data+ DA0-001 exam, this question tests your ability to distinguish supervised from unsupervised methods—a common trap is confusing clustering (like k-means) with regression or tree-based models. Remember that supervised learning always requires labeled data for training; if the algorithm uses known outcomes to learn, it’s supervised. A quick memory tip: think “supervised = supervisor gives the answers” (labels), so both linear regression and decision trees fit that role.
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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.
Which TWO of the following are examples of supervised learning algorithms?
Linear regression
Supervised regression algorithm.
K-means clustering
Why wrong: Unsupervised learning.
Principal component analysis (PCA)
Why wrong: Unsupervised dimensionality reduction.
Decision trees
Supervised classification/regression.
Apriori algorithm
Why wrong: Unsupervised association rule mining.
Answer choices
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 a supervised learning algorithm because it learns a mapping from input features to a continuous target variable using labeled training data. The model minimizes the difference between predicted and actual values (e.g., via ordinary least squares) to make predictions on new data.
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.
Linear regression
Why this is correct
Supervised regression algorithm.
Related concept
Read the scenario before looking for a memorised answer.
K-means clustering
Why it's wrong here
Unsupervised learning.
Principal component analysis (PCA)
Why it's wrong here
Unsupervised dimensionality reduction.
Decision trees
Why this is correct
Supervised classification/regression.
Related concept
Read the scenario before looking for a memorised answer.
Apriori algorithm
Why it's wrong here
Unsupervised association rule mining.
Common exam traps
CompTIA often tests the distinction between supervised and unsupervised learning by including clustering (K-means) and association (Apriori) as distractors, which candidates mistakenly think are supervised because they involve pattern discovery.
Detailed technical explanation
Supervised learning requires a labeled dataset where each input has a corresponding output; linear regression assumes a linear relationship and uses the coefficient of determination (R²) to measure fit. Decision trees split data recursively based on feature thresholds to minimize impurity (e.g., Gini impurity or entropy), making them interpretable but prone to overfitting without pruning. In practice, supervised algorithms like these are used for tasks such as predicting house prices (regression) or classifying customer churn (classification).
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 DA0-001 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
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Linear regression — Linear regression is a supervised learning algorithm because it learns a mapping from input features to a continuous target variable using labeled training data. The model minimizes the difference between predicted and actual values (e.g., via ordinary least squares) to make predictions on new data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This DA0-001 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 DA0-001 exam.
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