Question 924 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?
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 missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.
Median imputation is robust to outliers, and IQR-based capping is a standard method for handling outliers. Option B is wrong because removing rows with missing age can lead to data loss, and log transformation reduces skewness but does not handle outliers by capping. Option C is wrong because imputing with a constant like 0 is arbitrary and can bias the data, and capping using mean ± 3*std is sensitive to outliers. Option D is wrong because mean imputation is sensitive to outliers, and removing outliers via z-score can discard valid data points.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.
Why this is correct
Median is robust; IQR handles outliers.
- ✗
Remove rows with missing age and apply log transformation to 'amount'.
Why it's wrong here
Removing rows may reduce data.
- ✗
Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.
Why it's wrong here
Constant imputation can bias results; mean-based capping is not robust.
- ✗
Impute missing age values with the mean and remove outliers in 'amount' using z-score.
Why it's wrong here
Mean is sensitive to outliers.
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Last reviewed: Jun 20, 2026
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