DA0-002 Visualization and Reporting Practice Question
A data scientist creates a box plot of employee salaries and notices many outliers above the upper whisker. What action should be taken to best understand the salary distribution?
⚠ Common exam trap
The trap here is assuming that outliers should always be removed or that changing the visualization will solve the problem. Candidates may think that a histogram or trimming will 'fix' the box plot, but the key is to investigate the cause of outliers first.
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
✓
Investigate the outliers to determine if they are data entry errors or valid extremes
Outliers in a box plot represent data points that fall outside the typical range (beyond 1.5×IQR). They may be legitimate extreme values or errors. Investigating them is essential to understand whether they are valid (e.g., highly compensated executives) or mistakes (e.g., data entry errors). Removing or trimming them without investigation could distort the analysis and hide important insights.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the box plot with a histogram of the salaries
Why it's wrong here
A histogram shows frequency across bins but does not summarise quartiles, median or the interquartile range, so it answers a different question. It is tempting because histograms genuinely display distribution shape and skew, which is useful, but the stem asks to understand the flagged outliers specifically.
- ✗
Remove all outliers to create a more typical box plot
Why it's wrong here
Deleting outliers removes genuine high earners from the dataset, so the box plot no longer represents the actual salary distribution. It is tempting because outlier removal is valid when values are confirmed erroneous, such as measurement or entry errors, rather than real observations.
- ✗
Trim the top 5% of salaries and recreate the box plot
Why it's wrong here
Trimming discards the highest salaries, hiding the very skew the outliers reveal and distorting the distribution's tail. It is tempting because trimming is a legitimate technique for reducing the influence of extreme values in robust statistics, but here the outliers are the phenomenon under investigation.
- ✓
Investigate the outliers to determine if they are data entry errors or valid extremes
Why this is correct
Outliers above the upper whisker may represent genuine senior or executive salaries or data entry mistakes. Investigating each one distinguishes valid extremes from errors, giving an accurate picture of the salary distribution before deciding whether to exclude or retain them.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.