DA0-002 Data Analysis Practice Question
A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?
⚠ Common exam trap
The trap here is that candidates see 'predict future sales' and mistakenly choose linear regression, overlooking that time series methods are required when data has temporal dependencies and seasonality.
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
✓
Time series analysis
Time series analysis is specifically designed to model data points indexed in time order, making it ideal for capturing and forecasting seasonal patterns. Unlike regression models, it accounts for autocorrelation, trends, and seasonality components, which are critical for accurate sales prediction from historical data.
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 it's wrong here
Linear regression does not inherently handle seasonality.
- ✓
Time series analysis
Why this is correct
Time series analysis explicitly models seasonal patterns.
- ✗
K-means clustering
Why it's wrong here
Clustering is unsupervised and not for prediction.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is for classification, not forecasting.
Go deeper
Related to this question
About these practice questions
This DA0-002 question is part of Courseiva's 986-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.