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 (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.
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 is a categorical encoding technique.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not a scaling method.
- ✓
Standardization
Why this is correct
Standardization (Z-score) is a common feature scaling method.
- ✗
Binning
Why it's wrong here
Binning is discretization, not scaling.
- ✓
Normalization (Min-Max)
Why this is correct
Min-Max scaling is another common feature scaling technique.
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