DA0-002 Data Analysis Practice Question
A data analyst is examining the relationship between advertising spend and sales revenue across 50 regions. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following conclusions is most appropriate?
⚠ Common exam trap
The trap here is interpreting a high correlation as evidence of causation or confusing the correlation coefficient with the coefficient of determination.
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
✓
There is a strong positive linear relationship between advertising spend and sales revenue.
A Pearson correlation of 0.85 indicates a strong positive linear relationship between the two variables. It does not establish causation, nor does it directly state the proportion of variance explained. The correlation coefficient must be squared to get the coefficient of determination, and significance requires a hypothesis test. Thus, the appropriate conclusion is the strong positive linear association.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing advertising spend causes an increase in sales revenue.
Why it's wrong here
Correlation does not imply causation. A strong positive correlation indicates a linear association but does not prove that changes in advertising spend directly cause changes in sales revenue. There could be confounding variables such as seasonality or economic conditions. Therefore, this causal conclusion is not warranted from the correlation alone.
- ✗
The relationship is not statistically significant because the correlation is less than 0.90.
Why it's wrong here
Statistical significance depends on sample size and the p-value, not solely on the magnitude of the correlation. With 50 regions, a correlation of 0.85 is likely statistically significant. The threshold of 0.90 is arbitrary; significance is determined by hypothesis testing, not a fixed cutoff. Therefore, this conclusion is unfounded.
- ✗
Advertising spend explains 85% of the variation in sales revenue.
Why it's wrong here
The coefficient of determination (R-squared) indicates the proportion of variance explained, not the correlation coefficient itself. To get the percentage of variation explained, you must square the correlation: 0.85^2 = 0.7225, or about 72.25%. Thus, stating 85% is incorrect; it confuses the correlation with R-squared.
- ✓
There is a strong positive linear relationship between advertising spend and sales revenue.
Why this is correct
A Pearson correlation of 0.85 indicates a strong positive linear association. This means that as advertising spend increases, sales revenue tends to increase linearly. It does not imply causation, but it does summarize the strength and direction of the linear relationship. This is the most appropriate conclusion based solely on the correlation coefficient.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.