DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is performing EDA on a dataset with numerical features. Which methods are appropriate for identifying outliers? (Select TWO).
⚠ Common exam trap
DA0-002 often tests the confusion between descriptive statistics (mean, standard deviation, correlation) and actual outlier detection rules (Z-score, IQR), tempting candidates to select standard deviation alone as if it were a detection method.
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
✓
Z-score method
The Z-score method (C) is correct because it quantifies how many standard deviations a value lies from the mean, so observations with |z| above a threshold such as 3 (or 2.5) are flagged as outliers in numerical features. The Interquartile range (IQR) method (E) is also correct because it defines outliers as values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR, making it a robust, distribution-agnostic detection technique. Mean imputation (A) is not an outlier-detection method at all; it is a missing-value handling technique that replaces NaNs with the column mean. The Pearson correlation coefficient (B) measures the linear relationship between two variables, not the extremeness of individual values, so it cannot identify outliers. Standard deviation alone (D) only describes data spread and, without a rule like the Z-score, does not flag specific observations as 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.
- ✗
Mean imputation
Why it's wrong here
Mean imputation replaces missing values with the column average, altering the distribution and pulling extreme points toward the centre, which masks outliers rather than revealing them. It is tempting because imputation is a routine preprocessing step, and analysts often clean data before profiling it.
- ✗
Pearson correlation coefficient
Why it's wrong here
Pearson's coefficient measures linear association between two continuous variables, returning a value between -1 and 1; it cannot flag individual extreme values. It is tempting because correlation matrices are standard EDA output, and a coefficient near zero can hint at an outlier distorting the relationship.
- ✓
Z-score method
Why this is correct
The z-score method flags values whose standardised distance from the mean exceeds a threshold, typically ±3. It suits numerical features and satisfies the outlier-detection requirement by quantifying deviation in standard deviation units, assuming an approximately normal distribution.
- ✗
Standard deviation alone
Why it's wrong here
Standard deviation alone assumes a roughly normal distribution and a single symmetric spread; skewed or multimodal data produce thresholds that misclassify legitimate values. It is tempting because the three-sigma rule is widely taught, and it works acceptably when the feature is genuinely Gaussian.
- ✓
Interquartile range (IQR) method
Why this is correct
The IQR method flags values falling below Q1 − 1.5×IQR or above Q3 + 1.5×IQR. It satisfies the outlier-detection requirement for numerical features without assuming normality, making it robust to skewed distributions where z-scores mislead.
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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
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