DA0-002 Visualization and Reporting Practice Question
An analyst is presenting a recommendation to increase marketing spend. Which statement best follows the data-driven recommendation structure (evidence → insight → recommendation → expected impact)?
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
✓
Based on a 5% lift in sales from previous campaigns, we recommend a 10% increase in marketing spend, expecting a 7% revenue growth.
The correct structure provides evidence, insight derived from it, a recommendation, and the expected impact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A 10% increase in marketing spend is recommended because we have budget.
Why it's wrong here
Budget availability is a constraint, not evidence, and no expected impact is quantified, so the chain breaks at the first link. Justifying spend from remaining funds suits capacity planning, where the question is affordability rather than measured effect on an outcome.
- ✗
The data shows sales are up, so we should spend more on marketing.
Why it's wrong here
The statement asserts a recommendation from a correlation without stating the insight or quantifying expected impact, so it omits two required elements. It is tempting because it is concise and directional, and it would fit an informal stand-up update rather than a structured data-driven recommendation.
- ✓
Based on a 5% lift in sales from previous campaigns, we recommend a 10% increase in marketing spend, expecting a 7% revenue growth.
Why this is correct
The statement chains evidence (5% lift from prior campaigns), insight (spend drives sales), recommendation (10% increase) and expected impact (7% revenue growth), matching the required structure. The quantified forecast makes the recommendation testable rather than assertive.
- ✗
We should increase marketing spend by 10% because it might boost sales.
Why it's wrong here
"Might boost sales" supplies neither evidence nor a quantified expected impact, so the structure collapses to assertion. Hedged speculation is tempting when data is thin, but a forecast model with confidence intervals would be the right tool there, not a recommendation statement.
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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.