AI0-001 AI Models and Data Engineering Practice Question
Which TWO techniques are commonly used for feature selection in machine learning? (Choose 2)
⚠ Common exam trap
CompTIA often tests the distinction between dimensionality reduction (PCA) and feature selection, where candidates mistakenly think PCA selects original features rather than creating new ones.
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
✓
L1 regularization (Lasso)
L1 regularization (Lasso) is correct because it adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero, effectively performing feature selection by removing irrelevant features from the model. This makes it a built-in feature selection technique within the training process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is feature extraction, not selection.
- ✗
SMOTE
Why it's wrong here
SMOTE is for class imbalance.
- ✓
L1 regularization (Lasso)
Why this is correct
Lasso can zero out coefficients.
- ✗
Dropout
Why it's wrong here
Dropout is for regularization, not feature selection.
- ✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE selects features by importance.
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