DA0-002 Data Analysis Practice Question
A data analyst is preparing a dataset for analysis and needs to handle outliers. Which TWO of the following are common methods for treating 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
✓
Removal
Capping (winsorizing) limits extreme values, and removal simply deletes outlier rows. Transformation (e.g., log) can also reduce impact but is not listed here; normalization and imputation are not primary outlier treatments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Removal
Why this is correct
Removing outlier records is a common approach.
- ✓
Capping
Why this is correct
Capping replaces outliers with threshold values.
- ✗
Normalization
Why it's wrong here
Normalization scales data, not specifically for outliers.
- ✗
Imputation
Why it's wrong here
Imputation is for missing values.
- ✗
Standardization
Why it's wrong here
Standardization centers and scales, but does not treat outliers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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