DA0-002 Data Analysis Practice Question
A data analyst is cleaning a dataset and identifies several outliers. Which TWO methods are appropriate for handling outliers?
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
✓
Capping
Capping (winsorizing) and removal are common outlier treatments. Mean imputation is for missing values, and min-max normalization is scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Capping
Why this is correct
Replaces outliers with a threshold value.
- ✗
Mean imputation
Why it's wrong here
Used for missing values, not outliers.
- ✓
Removal
Why this is correct
Deletes outlier rows.
- ✗
Min-max normalization
Why it's wrong here
Scaling method, not for outlier handling.
- ✗
Forward-fill
Why it's wrong here
Used for missing time series data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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