AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO of the following are types of feature scaling?
⚠ Common exam trap
AWS often tests the distinction between feature scaling (changing the numeric range of features) and data transformation techniques like encoding or dimensionality reduction, leading candidates to confuse one-hot encoding or PCA with 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
Standardization (C) is a feature-scaling technique that transforms each feature to have a mean of 0 and a standard deviation of 1 (z-score, (x − μ)/σ), which is exactly what the question asks for. Normalization (Min-Max) (E) is also a feature-scaling method that rescales values into a fixed range, typically [0, 1], via (x − min)/(max − min). One-hot encoding (A) is a categorical-encoding technique that creates binary columns, not a scaling method. Principal Component Analysis (B) is a dimensionality-reduction technique that projects data onto principal components, not a scaling method. Binning (D) is a discretization technique that groups continuous values into bins, not a scaling method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary indicator columns; it changes representation, not numeric scale, so it is encoding rather than scaling. It is tempting because both are preprocessing transforms applied before training, and one-hot encoding would be correct when a nominal feature must be made numeric for the algorithm.
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Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality-reduction technique that projects data onto principal components, altering feature axes rather than rescaling their values onto a common range. It is tempting because it does transform numeric features, but it is used for compression and noise reduction, not for normalising magnitudes before distance-based algorithms.
- ✓
Standardization
Why this is correct
Standardization rescales each feature to zero mean and unit variance by subtracting the mean and dividing by the standard deviation, a distinct scaling technique alongside normalization, which instead maps values into a fixed range such as 0 to 1.
- ✗
Binning
Why it's wrong here
Binning discretises continuous variables into categorical intervals, changing a feature's type rather than its scale. It appeals because it also manipulates numeric values, yet it is used for handling non-linearity or outliers in decision-tree-style models, not for placing features on a comparable range as scaling requires.
- ✓
Normalization (Min-Max)
Why this is correct
Min-max normalisation rescales each feature to a fixed range, typically 0 to 1, by subtracting the minimum and dividing by the range. This is one of the two standard feature scaling types, alongside standardisation, so it directly satisfies the question's request for a scaling type.
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