AI0-001 AI Models and Data Engineering Practice Question
A data engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with 100 features. To improve model performance and reduce training time, the engineer decides to perform feature selection. Which two techniques are appropriate for this task? (Select TWO).
⚠ Common exam trap
CompTIA often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so candidates mistakenly select PCA thinking it selects features, when it actually transforms them into principal components.
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
✓
Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is an appropriate feature selection technique because it iteratively removes the least important features based on a model's feature importance scores or coefficients, directly reducing the feature count from 100 to a smaller subset. This improves model performance by eliminating irrelevant or redundant features and reduces training time by decreasing dimensionality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalization
Why it's wrong here
Normalization scales features, does not select them.
- ✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE selects features by removing the least important ones iteratively.
- ✓
L1 Regularization
Why this is correct
L1 regularization adds penalty that sets some feature coefficients to zero, effectively selecting features.
- ✗
One-Hot Encoding
Why it's wrong here
One-hot encoding is for converting categorical variables, not feature selection.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but creates new features, not selects original ones.
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