Quick Answer
The answer is mode imputation, which replaces missing values with the most frequently occurring category in the variable. This method is correct for categorical variables because it preserves the data’s natural distribution and avoids introducing invalid or nonsensical values, unlike mean or median imputation which are strictly designed for numerical data. On the CompTIA Data+ DA0-001 exam, this concept tests your understanding of appropriate imputation techniques for different data types, often appearing in scenario-based questions where a trap answer suggests using mean or median for categorical fields. A common memory tip is to remember that “mode” works for “most” categories, while “mean” and “median” are for numbers. When imputing missing values for categorical variables, always default to the mode unless the missingness is systematic or the category is too sparse for a reliable estimate.
This DA0-001 practice question tests your understanding of analyzing and modeling data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 missing values in a categorical variable representing customer region. Which imputation method is most appropriate?
Drop rows with missing values
Why wrong: Dropping rows may discard valuable data; imputation is preferred when appropriate.
Mode imputation
Mode is appropriate for categorical variables.
Mean imputation
Why wrong: Mean is for numeric data.
Median imputation
Why wrong: Median is for numeric 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
Mode imputation
Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.
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.
Drop rows with missing values
Why it's wrong here
Dropping rows may discard valuable data; imputation is preferred when appropriate.
Mode imputation
Why this is correct
Mode is appropriate for categorical variables.
Related concept
Read the scenario before looking for a memorised answer.
Mean imputation
Why it's wrong here
Mean is for numeric data.
Median imputation
Why it's wrong here
Median is for numeric data.
Common exam traps
The trap here is that candidates often confuse imputation methods across data types, incorrectly applying mean or median imputation to categorical variables because they focus on central tendency without considering data type appropriateness.
Detailed technical explanation
Under the hood, mode imputation works by computing the mode (most frequent value) of the non-missing entries in the categorical column and using that value to fill in missing cells. In real-world scenarios, such as customer region data, mode imputation is often used when the missing rate is low and the mode represents a dominant category, but it can introduce bias if the missing data is not missing at random (MNAR). A subtle behavior is that if multiple modes exist (multimodal distribution), the imputation may arbitrarily choose one, potentially distorting the data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Mode imputation — Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.
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 11, 2026
This DA0-001 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 DA0-001 exam.
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