DA0-002 Data Analysis Practice Question
A data analyst is preparing features for a machine learning model that uses distance-based algorithms (e.g., K-means, KNN). The dataset contains numerical features with different scales: age (0-100), income (20,000-200,000), and credit score (300-850). Which data transformation technique is most appropriate to ensure all features contribute equally to the distance calculations?
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 rescales features to a fixed range (e.g., 0 to 1), making distances computed equally weighted. Standardization is better for algorithms assuming Gaussian distributions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Z-score standardization
Why it's wrong here
Standardization centers to mean 0 and std 1, but for distance-based algorithms, min-max is often preferred to bound range.
- ✓
Min-max normalization
Why this is correct
Correct: scales all features to [0,1] so distances are not dominated by large-scale features.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not numerical scaling.
- ✗
Log transformation
Why it's wrong here
Log transformation reduces skewness but does not standardize scales.
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