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AI0-001 Implementing AI Solutions Practice Question

A data science team is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate and 1% fraudulent examples. Which TWO techniques should the team apply 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

Use class weights in the loss function

Oversampling the minority class (e.g., SMOTE) and using class weights during training are standard approaches to handle imbalanced data. Undersampling the majority class can also help but is less common here; train/test leakage is a separate issue; normalisation may not be needed.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use class weights in the loss function

    Why this is correct

    Class weights penalise misclassifications of the minority class more heavily.

  • Oversample the minority class using SMOTE

    Why this is correct

    SMOTE generates synthetic examples of the minority class, balancing the training set.

  • Undersample the majority class randomly

    Why it's wrong here

    Undersampling reduces data size; may discard useful information; not best when minority is very small.

  • Apply data normalisation (z-score) to all features

    Why it's wrong here

    Normalisation helps gradient descent but does not address class imbalance.

  • Randomly shuffle the dataset to prevent train/test leakage

    Why it's wrong here

    Shuffling is good practice but does not address class imbalance.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.