DA0-002 Data Analysis Practice Question
An analyst is preparing data for a clustering algorithm that uses Euclidean distance. Which TWO data preprocessing techniques should be applied to ensure all features contribute equally?
⚠ Common exam trap
DA0-002 often tests the confusion between scaling techniques and other preprocessing methods like log transformation or PCA. Candidates might think log transformation scales features, but it only reduces skewness; or that PCA is a scaling method, but it's for dimensionality reduction.
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
✓
Min-max normalization
Min-max normalization (A) is correct because it rescales each feature to a common range, typically [0, 1], via (x - min) / (max - min), so that no feature dominates the Euclidean distance calculation due to a larger scale. Z-score standardization (B) is also correct because it transforms each feature to mean 0 and standard deviation 1 using (x - μ) / σ, which equalizes the variance and ensures all features contribute equally to Euclidean distance. Log transformation (C) is not a scale-equalizing technique; it only compresses skewed distributions and does not guarantee equal feature contribution. Principal component analysis (D) is a dimensionality-reduction method, not a preprocessing step for equalizing feature scales. One-hot encoding (E) is for converting categorical variables into binary vectors and does not address differing numeric scales.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Min-max normalization
Why this is correct
Min-max normalization rescales each feature to a fixed 0–1 range, removing unit and magnitude disparities before Euclidean distance is computed. This equalises each feature's contribution, satisfying the requirement that no single large-range variable dominates the clustering metric.
- ✓
Z-score standardization
Why this is correct
Z-score standardization rescales each feature to zero mean and unit variance, so Euclidean distance treats every dimension on comparable scale. This satisfies the requirement that features contribute equally, since raw magnitude differences would otherwise dominate the distance calculation.
- ✗
Log transformation
Why it's wrong here
Log transformation compresses skew in a single feature's distribution; it does not place features on a common scale, so Euclidean distance still reflects differing units and ranges. It is tempting because it is a standard preprocessing step, and would be correct for heavily right-skewed variables such as income or counts.
- ✗
Principal component analysis
Why it's wrong here
PCA produces new uncorrelated components rather than placing existing features on a common scale, so features with larger ranges still dominate Euclidean distance. It is tempting because it addresses correlated and high-dimensional data, but the question requires scaling and normalisation, which PCA does not perform.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary indicator columns, expanding dimensionality without equalising the numeric scale of continuous features, so Euclidean distance stays unit-dependent. It is tempting because it is essential preprocessing, and would be correct when nominal categories must be represented numerically for the algorithm.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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