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 rescales each feature's magnitude; it leaves redundant columns in the dataset untouched, so correlated or duplicated features remain. Scaling is the right step when features differ wildly in units or range and distance-based or gradient-based algorithms converge poorly.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding expands categorical variables into binary indicator columns, increasing dimensionality rather than removing redundant features. It is the right technique when a categorical column must be converted for algorithms that cannot consume raw labels.
- ✓
Principal Component Analysis (PCA)
Why this is correct
Principal Component Analysis projects the many correlated features onto a smaller set of orthogonal components capturing most variance, eliminating redundancy. This directly addresses the stem's suspicion of redundant features by reducing dimensionality while retaining information.
- ✗
Polynomial features
Why it's wrong here
Polynomial features expand the feature set by generating interaction and power terms, which increases dimensionality rather than removing redundant inputs. It is tempting because it genuinely helps when a model underfits and non-linear relationships must be captured, but here the requirement is dimensionality reduction, which principal component analysis or correlation-based selection addresses.
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