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?
⚠ Common exam trap
Test-takers frequently confuse data transformation techniques (like normalization or imputation) with outlier-specific handling methods; candidates might select mean imputation or min-max normalization because they are common preprocessing steps, but they do not directly address 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 (A) is correct because it winsorizes extreme values by replacing outliers with a boundary value such as the 1st/99th percentile or a value derived from the IQR (e.g., Q1 − 1.5×IQR, Q3 + 1.5×IQR), preserving the record while limiting the outlier's influence. Removal (C) is correct because dropping outlier rows (or excluding them from analysis) is a standard, defensible approach when the values are confirmed to be erroneous or when their influence must be eliminated. Mean imputation (B) is not an outlier-handling method; it replaces missing values with the column mean and would actually be distorted by the very outliers present. Min-max normalization (D) merely rescales all values to the [0,1] range and does not reduce or eliminate outlier effects. Forward-fill (E) is a time-series missing-value technique that propagates the last valid observation and does nothing to address 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.
- ✓
Capping
Why this is correct
Capping replaces extreme values with a defined threshold, such as the 1st or 99th percentile, retaining the record while limiting its influence. This satisfies the scenario's need to handle outliers without discarding data, unlike deletion, which removes rows entirely and risks losing valid observations.
- ✗
Mean imputation
Why it's wrong here
Mean imputation replaces missing values with the column average, so it addresses gaps, not outliers; worse, outliers inflate the mean and distort every imputed value. It would be the correct method when a numeric field has missing entries and the distribution is roughly symmetric.
- ✓
Removal
Why this is correct
Removal deletes outlier records entirely, satisfying the stem's requirement for a legitimate handling method. It suits data-entry errors or impossible values where correction is unfeasible, preventing distortion of means and models. However, removal reduces sample size and biases results if outliers represent genuine variation, so it must be applied judiciously.
- ✗
Min-max normalization
Why it's wrong here
Min-max normalization rescales values into a fixed range but retains every outlier, which compresses normal data into a narrow band. It is the correct preprocessing step when features need comparable scales for distance-based algorithms, not a technique for removing or correcting anomalous observations.
- ✗
Forward-fill
Why it's wrong here
Forward-fill propagates the last observed value into missing entries, so it neither detects nor removes outliers; it can actually copy an outlier forward. It is the correct method for time-series gaps where the previous value is a reasonable estimate, not for handling anomalous observations.
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Written and reviewed by Johnson Ajibi, MSc IT Security
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.