DA0-002 Data Governance Practice Question
A data analyst is creating a report on customer satisfaction scores across different regions. The analyst wants to highlight regions that are significantly below average. Which of the following statistical methods is most appropriate for identifying these outliers?
⚠ Common exam trap
Test-takers frequently choose a bar chart with an average line (Option A) because it visually shows deviations, but it lacks a formal statistical criterion to define 'significantly below average,' which the IQR-based box plot provides.
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
✓
Box plot with interquartile range (IQR) to identify outliers.
A box plot with interquartile range (IQR) is the most appropriate method because it explicitly identifies outliers as data points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. This directly addresses the analyst's goal of highlighting regions significantly below average, as the IQR method is a standard statistical technique for detecting extreme values in a distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bar chart with average line.
Why it's wrong here
A bar chart with an average line displays each region's score against the mean, but it does not compute or quantify which regions are statistically significant outliers. It suits straightforward comparison; the question asks for a statistical method, such as z-scores or standard deviation.
- ✗
Pie chart of satisfaction categories.
Why it's wrong here
A pie chart shows each category's proportion of a whole, so it cannot express deviation from a mean or flag regions below average. It is the right choice for part-to-whole composition; identifying statistical outliers requires a chart plotting values against a computed average line.
- ✓
Box plot with interquartile range (IQR) to identify outliers.
Why this is correct
The IQR defines the middle 50% of scores; values falling below Q1 minus 1.5×IQR sit statistically apart from the distribution, flagging regions genuinely below average. A box plot displays this spread and its outliers directly, satisfying the requirement to highlight underperforming regions.
- ✗
Scatter plot of satisfaction vs. region.
Why it's wrong here
A scatter plot of satisfaction versus region plots categorical labels on one axis, producing meaningless vertical strips rather than a distribution, and computes no outlier threshold. It is correct for correlation between two numeric variables; identifying below-average regions needs a statistical outlier method.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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