Question 513 of 1,672
MLS-C01 Modeling Practice Question
Which TWO of the following are common techniques to handle missing values in a dataset?
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 with missing values
Options D and E are correct. D is correct because removing rows with missing values (listwise deletion) is a common approach when missing data is random and not extensive. E is correct because imputation with mean or median fills missing values with central tendency measures, preserving data size. A (standardization) is a scaling technique, not for missing values. B (PCA) is a dimensionality reduction method. C (one-hot encoding) is for converting categorical variables into numerical format, not for handling missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardization
Why it's wrong here
Standardization is for scaling features.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
- ✓
Remove rows with missing values
Why this is correct
Removing rows is a simple approach.
- ✓
Imputation with mean or median
Why this is correct
Imputation fills missing values with a central tendency.
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Last reviewed: Jun 20, 2026
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