DA0-002 Visualization and Reporting Practice Question
A data analyst is presenting a time-series chart of monthly sales to executives. The sales dropped sharply in March due to a one-time supply chain disruption. Which storytelling technique would best help the audience understand this anomaly?
⚠ Common exam trap
A common mix-up: candidates choose to smooth or remove the anomaly (options B or D) to make the chart look cleaner, failing to recognize that ethical data storytelling requires explaining, not hiding, significant events.
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
✓
Add an annotation explaining the supply chain disruption
Adding an annotation directly on the chart provides immediate context for the March sales drop, allowing executives to understand the anomaly without leaving the visualization. This technique follows the principle of 'contextual annotation' in data storytelling, where key events are marked to prevent misinterpretation of trends. It preserves data integrity while clarifying the cause, which is essential for accurate decision-making.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add an annotation explaining the supply chain disruption
Why this is correct
An annotation places explanatory text directly on the chart at March, tying the sharp drop to the one-time supply chain disruption. This contextualises the anomaly in place, so executives see cause and effect without a separate verbal explanation.
- ✗
Remove the March data point to avoid confusion
Why it's wrong here
Deleting the March point falsifies the series, removing the very anomaly the audience must understand and breaking the chart's integrity. Omitting outliers is tempting when they distort a trend line, and exclusion would be defensible only for confirmed erroneous data, not a real supply chain event requiring annotation.
- ✗
Use a different chart type to hide the drop
Why it's wrong here
Switching chart type conceals the March drop rather than explaining it, so executives cannot attribute the anomaly to the supply chain disruption. Hiding inconvenient data is tempting when a presenter fears a misleading-looking dip, yet a different chart type would be legitimate only for encoding data whose structure genuinely suits it, such as categorical comparisons.
- ✗
Use a moving average to smooth the drop
Why it's wrong here
A moving average deliberately flattens the March dip into surrounding months, erasing the one-time disruption instead of explaining it. Smoothing is tempting because it clarifies underlying trend in noisy series, and it would be correct when the goal is long-term direction, not when a specific anomaly must be communicated.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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