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AI0-001 Implementing AI Solutions Practice Question

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

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

Apply oversampling of the minority class using techniques like SMOTE

Oversampling the minority class (e.g., SMOTE) or undersampling the majority class are standard techniques to handle imbalanced datasets and improve recall on the minority 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.

  • Apply oversampling of the minority class using techniques like SMOTE

    Why this is correct

    SMOTE generates synthetic samples for the minority class, balancing the dataset and improving recall.

  • Remove all samples from the majority class to balance the dataset

    Why it's wrong here

    Removing all majority samples would discard most of the data and cause severe underfitting.

  • Normalize all numerical features to have zero mean and unit variance

    Why it's wrong here

    Normalization addresses scale differences but does not correct class imbalance.

  • Use a train-test split of 80-20 without any modification

    Why it's wrong here

    Without addressing imbalance, the model will likely predict the majority class for all instances.

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