DA0-002 Visualization and Reporting Practice Question
Which THREE are considered best practices in dashboard design? (Select three.)
⚠ Common exam trap
CompTIA often tests the misconception that adding visual flair (like 3D effects) or exhaustive data improves a dashboard, when in reality these choices degrade readability and violate core principles of effective data visualization.
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
✓
Using heat maps to visualize correlation
Option A is correct because heat maps encode values through color intensity across a matrix, making correlation and density patterns between two dimensions immediately visible without requiring users to read individual numbers. Option C is correct because maximizing the data-ink ratio, a principle from Edward Tufte, means removing non-data decoration so that most pixels convey actual information, which improves clarity and reduces cognitive load. Option D is correct because interactive filters let users drill into relevant subsets and explore the data themselves, turning a static report into an analytical tool that answers multiple questions. Option B is not a best practice because 3D charts distort proportions and depth perception, making values harder to compare accurately than their 2D equivalents. Option E is not a best practice because cramming every data point into a dashboard creates clutter and obscures the key signals, whereas dashboards should surface aggregated or prioritized metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using heat maps to visualize correlation
Why this is correct
Heat maps encode correlation coefficients as colour intensity across a matrix of variable pairs, letting viewers spot strong positive or negative relationships instantly. This satisfies dashboard design best practice by compressing dense statistical relationships into a single glanceable visual.
- ✗
Using 3D charts to add depth
Why it's wrong here
3D charts distort bar heights and pie angles through perspective, so viewers misread relative magnitudes — the opposite of accurate comparison. They are tempting for visual impact in executive presentations, but two-dimensional bars or lines convey the same data without perceptual error.
- ✓
Maximizing the data-ink ratio
Why this is correct
Maximising the data-ink ratio removes redundant gridlines, borders and decoration so every drop of ink encodes data, improving clarity and speeding interpretation. This satisfies dashboard design best practice by reducing cognitive load while preserving the full information content.
- ✓
Providing interactive filters for exploration
Why this is correct
Interactive filters let users slice data by their own criteria, satisfying the stem's exploration requirement rather than forcing a fixed narrative. This aligns with dashboard design best practice: enabling self-service drill-down and parameter selection keeps the view actionable across audiences, unlike static charts that answer only the designer's original question.
- ✗
Including every data point in the dashboard
Why it's wrong here
Cramming every data point creates clutter that obscures the metrics driving decisions; dashboards should surface only what supports the audience's questions. It is tempting because completeness feels rigorous, but exhaustive detail belongs in underlying reports or drill-through views, not the primary dashboard surface.
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