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AI Models and Data EngineeringeasyMultiple ChoiceObjective-mapped

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 normalizes feature ranges but does not address class imbalance.

  • Dimensionality reduction

    Why it's wrong here

    Dimensionality reduction reduces the number of features, which may affect performance but does not address imbalance.

  • Polynomial features

    Why it's wrong here

    Polynomial features add interaction terms, which can increase model complexity but not fix imbalance.

  • Oversampling

    Why this is correct

    Oversampling (e.g., SMOTE) creates synthetic samples of the minority class to balance the dataset.

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