AI0-001 AI Models and Data Engineering Practice Question
A data scientist is preparing a dataset for a classification model. The dataset contains a column "Age" with 10% missing values and a column "Income" with 30% missing values. Which imputation strategy is MOST appropriate to minimize bias?
⚠ Common exam trap
CompTIA often tests the misconception that a single imputation method (e.g., mean for all columns) is universally appropriate, when in fact the choice must consider the missingness rate and the distribution of each feature to minimize bias.
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
✓
Replace missing Age with the mean and missing Income with the median.
Using mean imputation for Age (10% missing) and median imputation for Income (30% missing) minimizes bias. Mean is suitable for roughly symmetric distributions with low missingness, while median is robust to outliers and skewness, which is common in income data. This combination reduces distortion of central tendency and preserves data integrity better than uniform methods.
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 missing Age with the mean and missing Income with the median.
Why this is correct
Mean for symmetric Age, median for skewed Income minimizes bias.
- ✗
Delete all rows with missing values.
Why it's wrong here
Deleting all rows with missing values would discard 30% of the data due to the Income column, drastically reducing sample size and introducing selection bias if missingness correlates with income level. This approach is tempting because listwise deletion is valid when data are missing completely at random (MCAR) and the remaining sample remains representative, but here the high proportion of missing values violates that assumption.
- ✗
Replace missing Age with the mode and missing Income with a constant value.
Why it's wrong here
Mode is not robust for numerical data; constant value ignores variability.
- ✗
Replace missing values with zeros.
Why it's wrong here
Zeros are artificial and distort the original distribution.
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