AI0-001 AI Models and Data Engineering Practice Question
A data scientist notices that a binary classification model consistently predicts the majority class. Which data engineering technique should be applied?
⚠ Common exam trap
CompTIA often tests the misconception that feature scaling or dimensionality reduction can fix class imbalance, when in reality these techniques address different issues like feature magnitude or curse of dimensionality, not skewed target distributions.
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
✓
Oversampling
Oversampling (Option D) is correct because the model's bias toward the majority class indicates a class imbalance problem. By synthetically increasing the number of minority class samples (e.g., using SMOTE or random oversampling), the training data becomes more balanced, allowing the classifier to learn decision boundaries that are not skewed toward the majority class.
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
Feature scaling normalises numeric ranges for distance-based or gradient-based learners; it does not alter the ratio of majority to minority labels, so predictions stay skewed. It is tempting as routine preprocessing, but the stem's symptom is class imbalance, which resampling or class weighting addresses instead.
- ✗
Dimensionality reduction
Why it's wrong here
Dimensionality reduction compresses feature space via PCA or similar, leaving the class distribution untouched, so the model still predicts the majority class. It is tempting when many correlated features slow training, but the stem describes imbalance, which resampling or class weights correct.
- ✗
Polynomial features
Why it's wrong here
Polynomial features expand the input space to capture non-linear relationships, which does nothing when the model already ignores the minority class. It is tempting for underfitting on continuous predictors, but the described symptom is class imbalance, addressed by resampling or class weighting rather than feature engineering.
- ✓
Oversampling
Why this is correct
Consistent majority-class prediction indicates severe class imbalance, where the loss is minimised by always guessing the dominant label. Oversampling replicates or synthesises minority-class examples, rebalancing the training distribution so the classifier learns the minority decision boundary.
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