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?
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 and Z-score standardization both scale features to comparable ranges, preventing features with larger scales from dominating distance calculations.
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
Scales features to [0,1] range.
- ✓
Z-score standardization
Why this is correct
Centers to mean=0 and scales to std=1.
- ✗
Log transformation
Why it's wrong here
Log transformation reduces skewness but does not standardize scale across features.
- ✗
Principal component analysis
Why it's wrong here
PCA reduces dimensionality, not directly for equal contribution.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not scaling.
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