This AI0-001 practice question tests your understanding of ai models and data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?
Mean imputation
Why wrong: Mean is affected by outliers.
Median imputation
Median is robust to non-normal distributions.
Mode imputation
Why wrong: Mode is for categorical data.
Remove rows with missing values
Why wrong: Loses 20% of data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Median imputation
Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Mean imputation
Why it's wrong here
Mean is affected by outliers.
Median imputation
Why this is correct
Median is robust to non-normal distributions.
Related concept
Read the scenario before looking for a memorised answer.
Mode imputation
Why it's wrong here
Mode is for categorical data.
Remove rows with missing values
Why it's wrong here
Loses 20% of data.
Common exam traps
CompTIA often tests the misconception that mean imputation is always the default or best choice for numerical data, but the trap here is that candidates overlook the importance of distribution shape and outlier sensitivity, leading them to select mean imputation despite the data not being normally distributed.
Detailed technical explanation
Under the hood, median imputation minimizes the impact of outliers by using the 50th percentile, which is a robust statistic in the presence of skewness or heavy tails. In real-world datasets like customer demographics, age often exhibits right-skewness due to older outliers, making median imputation a standard preprocessing step in scikit-learn's SimpleImputer with strategy='median'. This approach aligns with best practices in data engineering for maintaining statistical properties before feeding data into machine learning algorithms.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Median imputation — Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI0-001 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 AI0-001 exam.
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