MLS-C01 Missing value imputation Practice Question
Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)
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
✓
Remove rows or columns with missing values if they are few.
The correct techniques for handling missing values are removing rows/columns with missing values (if the proportion is small) and imputing missing values with statistical measures like the mean or median. Options A (feature scaling), C (PCA), and D (one-hot encoding) are not methods for dealing with missing data; they serve other purposes such as normalization, dimensionality reduction, and encoding categorical variables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply feature scaling to normalize the data.
Why it's wrong here
Feature scaling does not handle missing values.
- ✓
Remove rows or columns with missing values if they are few.
Why this is correct
Deletion is a valid approach when missing data is minimal.
- ✗
Use Principal Component Analysis (PCA) to reduce dimensionality.
Why it's wrong here
PCA is for dimensionality reduction, not missing value treatment.
- ✗
Apply one-hot encoding to the missing values.
Why it's wrong here
One-hot encoding is for categorical variables, not for missing values.
- ✓
Impute missing values with the mean or median of the column.
Why this is correct
Mean/median imputation is a common technique.
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