DA0-002 Data Analysis Practice Question
A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?
⚠ Common exam trap
DA0-002 often tests the trade-off between preserving sample size and introducing bias — candidates pick deletion for 'cleanliness' or zero-fill for simplicity without considering the distortion each introduces.
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
✓
Impute with the mean age
When data is missing completely at random (MCAR) and the analyst wants to preserve sample size, mean imputation is the standard approach — it replaces missing values with the average of the observed values, retaining all rows and avoiding the bias that deletion would introduce. For MCAR data, mean imputation produces unbiased estimates of the mean (though it reduces variance).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Forward-fill using the previous value
Why it's wrong here
Forward-fill copies the preceding record's age onto an unrelated customer, fabricating values because row order carries no temporal meaning here. It is tempting because forward-fill is standard for time-series sensor gaps, where the previous reading genuinely predicts the next.
- ✓
Impute with the mean age
Why this is correct
Mean imputation replaces each missing age with the variable's average, retaining every record and therefore preserving sample size. Because the data is missing completely at random, the missingness is unrelated to any variable, so mean substitution introduces minimal bias compared with deletion methods.
- ✗
Replace missing values with zero
Why it's wrong here
Zero is not a plausible customer age, so imputing it distorts the mean, variance and any age-based segmentation. It is tempting because zero-filling is trivial to implement, but it suits count variables where zero is a genuine, meaningful observed value.
- ✗
Delete all rows with missing data
Why it's wrong here
Listwise deletion discards every record containing a missing age, shrinking the sample and biasing results, which contradicts the stated goal of preserving sample size. It is tempting because complete-case analysis is valid and simple when missingness is rare and random.
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JA
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.