DA0-002 Data Analysis Practice Question
A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?
⚠ Common exam trap
It's easy for candidates to confuse simple linear regression with multiple linear regression, thinking that 'linear regression' alone suffices, but the exam specifically tests whether you recognize that multiple predictors require multiple regression.
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
✓
Multiple linear regression
Multiple linear regression is the correct technique because the analyst needs to model a continuous outcome (sales) based on two or more predictor variables: advertising spend (continuous) and season (categorical, typically encoded as dummy variables). This allows the model to capture the independent effect of each predictor on sales, which simple linear regression cannot do because it only handles one predictor.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Simple linear regression
Why it's wrong here
Simple linear regression uses only one predictor; the scenario has two.
- ✓
Multiple linear regression
Why this is correct
Multiple linear regression handles two or more predictors and predicts a continuous outcome.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is for binary classification, not predicting a continuous value like sales.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised learning algorithm for grouping data, not prediction.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 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 DA0-002 exam.