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) (B) is a wrapper-based feature selection method that repeatedly trains a model, ranks features by importance (e.g., coefficients or feature importances), and prunes the least important ones until the desired number of features remains, directly reducing the 100-feature space to improve performance and cut training time. L1 Regularization (C) — Lasso — adds a penalty equal to the absolute value of coefficients to the loss function, which drives many feature coefficients exactly to zero, effectively performing embedded feature selection and yielding a sparse model. Normalization (A) is a scaling preprocessing step (e.g., min-max or z-score) that changes feature magnitudes but does not remove features, so it is not feature selection. One-Hot Encoding (D) is a categorical-encoding transformation that expands categorical variables into binary columns, increasing dimensionality rather than reducing it. Principal Component Analysis (E) is a dimensionality-reduction technique that creates new uncorrelated components from linear combinations of the original features, but it is not feature selection because it does not retain or select the original features.
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
Recursive Feature Elimination fits a model, ranks features by importance, removes the weakest, and repeats, progressively shrinking the 100-feature set. This reduces training time and can improve performance by eliminating irrelevant or noisy predictors from the dataset.
- ✓
L1 Regularization
Why this is correct
L1 (Lasso) regularization adds a penalty equal to the absolute value of coefficients, driving irrelevant feature weights to exactly zero. With 100 features and only 10,000 samples, this embedded method performs feature selection during training, satisfying the requirement to reduce dimensionality and cut training time.
- ✗
One-Hot Encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary indicator columns; applied to 100 features it typically expands dimensionality, the opposite of selection. It is tempting because it is a routine encoding step, and would be correct when categorical predictors must be represented numerically for the model.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality-reduction technique that projects features onto new uncorrelated components, not a feature-selection method that retains original features. It is tempting because it does shrink the feature count and training time, and would be correct if interpretability of individual features were not required.
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