DA0-002 Data Governance Practice Question
A data analyst is communicating insights about a sales forecast to stakeholders. Which three of the following should the analyst include to build trust and clarity? (Select THREE.)
⚠ Common exam trap
CompTIA often tests the distinction between transparency and information overload, so the trap here is that candidates think sharing raw data (Option B) is always good practice, but in stakeholder communication, raw data without context or summary statistics can confuse rather than clarify.
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
✓
The confidence intervals around the forecast.
Option C is correct because confidence intervals quantify the range within which the true forecast value is likely to fall, giving stakeholders an honest measure of the forecast's precision rather than a single misleading point estimate. Option D is correct because stating the assumptions made in the forecast model (e.g., growth rates, seasonality, market conditions) lets stakeholders judge whether those premises match reality and understand how the results were derived. Option E is correct because discussing potential risks and uncertainties prepares stakeholders for scenarios where the forecast may not hold, which builds credibility and supports better contingency planning. Option A does not belong because presenting only the most optimistic scenario hides downside risk and biases decisions, undermining trust. Option B does not belong because dumping raw data on stakeholders adds noise without interpretation; the analyst should present summarized, relevant evidence instead of the full raw dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Only the most optimistic scenario.
Why it's wrong here
Presenting only the optimistic scenario hides risk and misleads decision-makers, undermining the clarity the question demands. It is tempting because a headline figure feels persuasive, and it would be correct when stakeholders explicitly request a best-case planning assumption alongside documented uncertainty ranges.
- ✗
The raw data used for the forecast.
Why it's wrong here
Dumping raw data overwhelms stakeholders and obscures the narrative, since it lacks the aggregation and context that communicate findings. It is tempting because transparency builds trust, and it would be correct when presenting to technical reviewers who must validate the methodology and calculations themselves.
- ✓
The confidence intervals around the forecast.
Why this is correct
Confidence intervals quantify the uncertainty around the forecast, directly satisfying the need for clarity about prediction reliability. Stakeholders can judge the plausible range of sales outcomes rather than treating a single point estimate as certain, which builds trust through honest disclosure of model precision and variability.
- ✓
The assumptions made in the forecast model.
Why this is correct
Documenting the forecast model's assumptions lets stakeholders judge whether the underlying conditions match their business reality. Disclosing them satisfies the stem's trust requirement, since hidden assumptions are the usual source of misplaced confidence in projections.
- ✓
A discussion of potential risks and uncertainties.
Why this is correct
Discussing risks and uncertainties directly satisfies the trust and clarity requirement by exposing the forecast's confidence limits and underlying assumptions. Stakeholders can then judge how much weight to place on projections, rather than treating point estimates as certainties. This transparency about model limitations and external variables is what distinguishes credible analytical communication from unqualified assertions.
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