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DA0-002 Data Analysis Practice Question

A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?

⚠ Common exam trap

It's easy for candidates to confuse 'linear trend' with 'linear in parameters' and incorrectly choose polynomial regression, thinking it adds flexibility, when the question explicitly states no seasonality and a linear trend, making simple linear regression the optimal choice.

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 technique because the data shows a linear trend with no seasonality, making a straight-line model the simplest and most effective fit. It directly models the relationship between the independent variable (e.g., time) and the dependent variable (monthly sales) using a linear equation, minimizing the sum of squared residuals.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Polynomial regression

    Why it's wrong here

    Polynomial regression adds complexity that may overfit a simple linear trend.

  • Logistic regression

    Why it's wrong here

    Logistic regression is used for classification, not continuous prediction.

  • Linear regression

    Why this is correct

    Linear regression directly models a linear relationship between independent and dependent variables.

  • Ridge regression

    Why it's wrong here

    Ridge regression is a regularized linear regression; while usable, it is not the most appropriate for a simple linear trend.

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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.