DA0-002 Data Governance Practice Question
When presenting data insights, which three practices help ensure clarity? (Choose THREE.)
⚠ Common exam trap
Test-takers frequently think including all data points (Option A) ensures completeness, but the exam tests the understanding that clarity often requires simplification and focus, not exhaustive detail.
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
✓
Use consistent color coding
Option C is correct because consistent color coding lets viewers instantly associate a color with the same category, metric, or series across every chart, slide, or dashboard, reducing cognitive load and preventing misinterpretation. Option D is correct because clearly labeling axes—including units, scale, and time period—ensures the audience knows exactly what is being measured and how to read the values, which is essential for accurate interpretation. Option E is correct because providing context and benchmarks (such as targets, prior-period comparisons, or industry averages) turns raw numbers into meaningful insights and helps the audience judge whether a result is good, bad, or expected. Option A does not belong because including every data point creates clutter and obscures the key message; effective visualizations simplify and highlight what matters. Option B does not belong because overlaying multiple chart types on a single axis is confusing and can distort scale relationships, so dual axes or separate charts are preferred when different measures must be shown.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Include all data points in the chart
Why it's wrong here
Plotting every data point overloads the visual, burying the insight the audience needs. Including all points suits exploratory analysis or data-quality auditing, where completeness matters, but for presenting insights the chart should show only the subset that supports the message.
- ✗
Overlay multiple chart types on one axis
Why it's wrong here
Combining chart types on a shared axis distorts scale and comparison, since bars, lines and areas encode values differently. Overlaying series is legitimate for dual-axis trend correlation, but that requires separate axes; a single axis cannot represent both measures honestly.
- ✓
Use consistent color coding
Why this is correct
Consistent colour coding assigns stable meaning to each category across every chart, so viewers learn the mapping once and apply it throughout. This satisfies the clarity constraint by removing the cognitive cost of reinterpreting colours slide by slide.
- ✓
Label axes clearly
Why this is correct
Labelled axes state the measure, unit and scale explicitly, so viewers interpret values without guessing. This satisfies the clarity constraint by eliminating ambiguity about what each axis represents, which is essential when the audience cannot interrogate the underlying data.
- ✓
Provide context and benchmarks
Why this is correct
Context and benchmarks anchor each figure against a meaningful reference, such as prior periods, targets or industry averages, so the audience can judge whether a value is good, bad or typical. Without them, numbers lack scale, directly satisfying the clarity requirement in the stem.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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