DA0-002 Data Governance Practice Question
Which TWO of the following are effective techniques for presenting data to a non-technical audience?
⚠ Common exam trap
CompTIA often tests the misconception that non-technical audiences need more data (tables, statistics) to understand insights, when in fact they need less—focusing on simplicity, visual emphasis, and clear language—so candidates mistakenly choose options A, B, or D thinking they are thorough.
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
✓
Highlight the most important insights using callouts.
Option C is correct because callouts draw a non-technical audience's attention directly to the key insights, letting them grasp the main message without needing to interpret raw data or charts themselves. Option E is correct because using simple language and avoiding jargon makes the presentation accessible to listeners who lack a technical or statistical background, ensuring the findings are understood rather than obscured. Options A and B do not belong because explaining statistical methods and providing detailed data tables cater to a technical or analytical audience and tend to overwhelm non-technical listeners with unnecessary detail. Option D is also incorrect because using many different colors can create visual clutter and confusion rather than clarifying the data for a general audience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Explain the statistical methods used in the analysis.
Why it's wrong here
Explaining statistical methods introduces jargon and methodological detail that non-technical listeners cannot act on, distracting from the findings themselves. It is tempting because methodological transparency builds credibility with analytical peers, and would be correct when presenting to a technical review panel or validating the analysis.
- ✗
Include detailed data tables for reference.
Why it's wrong here
Detailed tables force non-technical audiences to scan raw numbers and derive their own conclusions, obscuring the headline message. It is tempting because tables provide complete reference data, which is correct for an appendix or a technical audience that needs to verify individual figures.
- ✓
Highlight the most important insights using callouts.
Why this is correct
Callouts place short annotations directly beside the relevant data points, directing a non-technical audience's attention to the key insight without requiring them to interpret axes or legends. This satisfies the constraint of presenting effectively to viewers lacking analytical background, reducing cognitive effort.
- ✗
Use many different colors to distinguish data points.
Why it's wrong here
Many colours overload the visual channel, making categories harder to distinguish and implying meaning where none exists. It is tempting because colour separates series clearly, which is correct when a small number of categories needs distinct encoding and the palette is accessible.
- ✓
Use simple language and avoid jargon.
Why this is correct
Plain language removes statistical and technical terminology that a non-technical audience cannot interpret, so the message is understood without translation. This satisfies the constraint of presenting effectively to non-specialists, ensuring the insight, not the vocabulary, carries the meaning.
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