MLS-C01 Modeling Practice Question
Which TWO techniques are used for feature scaling? (Choose 2.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between feature scaling techniques (which transform numerical feature values) and encoding or dimensionality reduction techniques, leading candidates to mistakenly select one-hot encoding or PCA as scaling methods.
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
✓
Standardization (Z-score normalization)
Standardization (Z-score normalization) is a feature scaling technique that transforms data to have a mean of 0 and a standard deviation of 1, using the formula z = (x - μ) / σ. This is essential for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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One-hot encoding
Why it's wrong here
One-hot encoding transforms categorical variables into binary columns, but it does not alter the numerical range or distribution of features, which is the core requirement of feature scaling. It is tempting because it is a common preprocessing step for algorithms like linear regression, yet it serves to represent nominal data, not to normalise or standardise continuous values.
- ✓
Standardization (Z-score normalization)
Why this is correct
Standardization scales features to have mean 0 and variance 1.
- ✓
Min-Max scaling
Why this is correct
Min-Max scaling scales features to a fixed range, usually [0,1].
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Principal Component Analysis (PCA)
Why it's wrong here
PCA is dimensionality reduction, not scaling.
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Label encoding
Why it's wrong here
Label encoding is for ordinal categorical variables.
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