DA0-002 Data Analysis Practice Question
A dataset contains outliers in a feature that will be used for linear regression. Which two outlier treatment methods are appropriate? (Choose 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
✓
Cap the outliers at a percentile (e.g., 99th percentile)
Capping outliers or transforming the variable can reduce their influence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cap the outliers at a percentile (e.g., 99th percentile)
Why this is correct
Capping limits extreme values.
- ✗
Use min-max normalization
Why it's wrong here
Normalization does not treat outliers.
- ✗
Increase the sample size
Why it's wrong here
More data does not remove existing outliers.
- ✓
Remove the outlier rows
Why this is correct
Removing outliers can improve model fit if they are errors.
- ✗
Replace outliers with the mean
Why it's wrong here
Replacing with mean may introduce bias and is not standard.
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