DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which of the following is a valid method for handling missing numerical data?
⚠ Common exam trap
It's easy for candidates to confuse 'ignoring' missing values with a valid handling method, or assuming that any replacement (like zeros) is acceptable without considering the data's meaning.
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
Imputing missing numerical values with the mean is a standard and valid statistical approach for handling missing data in a numerical column. It preserves the overall distribution's central tendency and allows the analyst to retain all other rows for analysis. This method is especially appropriate when the missingness is random and the column is roughly normally distributed, minimizing bias introduced by dropping records.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the entire column
Why it's wrong here
Dropping the column discards every age observation, destroying a variable the analysis presumably needs, rather than addressing the gaps. It is tempting because removing a field is valid when that attribute is irrelevant or almost entirely null, but here only several values are missing, so imputation or row deletion preserves the data.
- ✗
Ignore the missing values
Why it's wrong here
Ignoring missing values leaves the age field unusable for calculations such as mean or regression, since most tools propagate or skip those rows silently, biasing results. It is tempting because listwise omission is a legitimate tactic when missingness is tiny and completely random, but here several values are affected.
- ✓
Impute with the mean
Why this is correct
Imputing with the mean replaces each missing 'age' entry with the column's arithmetic average, preserving the existing sample size and keeping the dataset's overall mean unchanged. This satisfies the stem's requirement for a valid numerical handling method, since the mean is calculable only on quantitative data such as age.
- ✗
Replace with zeros
Why it's wrong here
Replacing missing ages with zeros introduces a false value that distorts the mean, variance and any age-based analysis, because zero is a valid age that never occurs in the population. It is tempting because zero-filling is a quick, common imputation for counts and sparse numeric fields, but for a bounded variable like age the correct methods are mean or median imputation, or dropping the affected rows.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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