DA0-002 Visualization and Reporting Practice Question
A data analyst creates a bubble chart showing country GDP (x-axis), life expectancy (y-axis), and population (bubble size). However, large bubbles overlap and obscure many data points. Which corrective action should the analyst take?
⚠ Common exam trap
Test-takers frequently choose to reduce bubble sizes uniformly (Option C) thinking it solves overlap, but this distorts the proportional encoding of population, whereas opacity preserves the original data relationships while improving visibility.
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
✓
Set bubble opacity to 70%
Setting bubble opacity to 70% allows overlapping bubbles to become semi-transparent, so data points underneath remain visible. This technique preserves the original data representation (GDP, life expectancy, and population) without altering the chart's scale or removing data. It is a standard visualization practice for handling overplotting in dense scatter plots and bubble charts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the chart canvas size
Why it's wrong here
Enlarging the canvas spreads points further apart but leaves each bubble's pixel diameter unchanged, so overlapping persists wherever GDP and life expectancy values cluster. It is tempting because canvas size genuinely helps when axes are cramped or labels collide, yet it cannot resolve marker-area collisions.
- ✓
Set bubble opacity to 70%
Why this is correct
Setting bubble opacity to 70% lets overlapping marks remain partially visible, so obscured data points stay readable while bubble size still encodes population. This directly addresses the stem's constraint that large bubbles overlap and hide points, without altering the GDP or life expectancy axes.
- ✗
Reduce all bubble sizes uniformly
Why it's wrong here
Uniformly shrinking every bubble preserves the population ratios but still leaves the largest circles overlapping in dense regions, and small nations become unreadable. Scaling size by area is the real fix; uniform reduction is tempting because it is quick and keeps relative proportions visually intact.
- ✗
Remove outlier countries with large populations
Why it's wrong here
Removing large-population countries discards legitimate data and biases the chart, hiding the very relationship being analysed. Overlap is a rendering problem solved by reducing bubble scale, adding transparency or jitter, or switching to a treemap or log-scaled axes. Outlier removal suits genuine measurement errors, not valid observations.
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