AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between feature reduction (PCA) and feature transformation (scaling, encoding, polynomial expansion) to see if candidates confuse techniques that change feature count versus those that only change feature values.
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
✓
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature scaling
Why it's wrong here
Scaling normalizes values but does not reduce number of features.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding increases the number of features, not reduces.
- ✓
Principal Component Analysis (PCA)
Why this is correct
PCA reduces dimensionality by transforming correlated features into uncorrelated components, eliminating redundancy.
- ✗
Polynomial features
Why it's wrong here
Polynomial features increase dimensionality, making redundancy worse.
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