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?
⚠ Common exam trap
The trap is confusing data scaling techniques (normalization, standardization) with outlier treatment methods; candidates must distinguish between transforming the scale and actually handling extreme values.
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
Removal (A) is a common outlier treatment because it deletes the extreme data points from the dataset, which is appropriate when outliers are errors or when their influence must be eliminated before analysis. Capping (B), also called winsorizing, is a common treatment that replaces extreme values with a defined threshold, such as the 1st/99th percentile or a value at a set number of standard deviations, preserving the record while limiting the outlier's effect. Normalization (C) is a scaling technique that rescales values to a fixed range like 0–1; it does not identify or treat outliers, so it is not a treatment method. Imputation (D) fills in missing values rather than addressing extreme values, so it is not an outlier treatment. Standardization (E) transforms data to have mean 0 and standard deviation 1, which is also a scaling method and does not by itself treat outliers.
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
Removal deletes or filters out records whose values fall beyond a defined threshold, such as three standard deviations or the interquartile range fence. This satisfies the need to treat outliers by eliminating their distortion, though it reduces sample size and risks discarding legitimate extreme observations.
- ✓
Capping
Why this is correct
Capping replaces extreme values with a defined boundary, such as the 99th percentile or a fixed threshold, retaining the record while limiting its influence. This satisfies the need to treat outliers without discarding data, preserving sample size and the observation's other field values.
- ✗
Normalization
Why it's wrong here
Normalization scales data, not specifically for outliers.
- ✗
Imputation
Why it's wrong here
Imputation replaces missing values with estimated ones; it does not treat outliers, which are present, extreme values. It is tempting because imputation is a recognised data-cleaning step, and it is the correct choice when the problem is nulls rather than values lying far outside the expected distribution.
- ✗
Standardization
Why it's wrong here
Standardization centers and scales, but does not treat outliers.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.