DA0-002 Data Governance Practice Question
A data analyst needs to present findings about customer churn to business stakeholders. The analysis identified that churn is highest among customers who have called customer support more than three times in the last month. Which of the following is the best way to communicate this insight?
⚠ Common exam trap
A common mix-up: candidates choose a scatter plot (Option A) because they think it shows 'relationship', but they fail to recognize that a scatter plot is inappropriate for a binary dependent variable and discrete independent variable, whereas a bar chart is the correct choice for comparing rates across categories.
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
✓
A bar chart comparing churn rates for different support call counts.
A bar chart directly compares churn rates across discrete categories of support call counts (e.g., 0, 1, 2, 3, 4+ calls), making it easy for stakeholders to see the spike at 'more than three calls'. This aligns with the insight that churn is highest among customers with >3 support calls, and a bar chart is the standard visualization for comparing a continuous metric (churn rate) across categorical bins.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A scatter plot to show the relationship between support calls and churn.
Why it's wrong here
A scatter plot is better for continuous variables; here the independent variable is categorical.
- ✗
A pie chart showing the proportion of churned vs. retained customers.
Why it's wrong here
A pie chart shows overall proportions, not the relationship with support calls.
- ✓
A bar chart comparing churn rates for different support call counts.
Why this is correct
A bar chart effectively shows the relationship between a categorical variable (call count bins) and churn rate.
- ✗
A table of raw churn data by customer ID.
Why it's wrong here
Raw data tables are overwhelming and do not highlight the pattern.
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Written by Johnson Ajibi, MSc IT Security
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