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AI0-001 Implementing AI Solutions Practice Question

A company is building an AI-powered document intelligence system to extract key fields from scanned invoices. The data contains 95% of invoices from one vendor and 5% from others. During model training, the F1 score is 0.95 on the overall test set, but the performance on the minority vendor invoices is very poor. What is the MOST likely cause?

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

The dataset is imbalanced, and the model is biased toward the majority class

The imbalanced dataset causes the model to learn mostly from the majority class, leading to poor performance on the minority class. The other options are less likely given the high overall F1 score.

Answer analysis

Option-by-option breakdown

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

  • The model is overfitting on the minority class

    Why it's wrong here

    Overfitting on the minority class would lead to high performance on minority and poor on majority, which is not the case.

  • The dataset is imbalanced, and the model is biased toward the majority class

    Why this is correct

    Imbalanced data causes the model to optimize for the majority class, ignoring minority classes.

  • The data has a train/test leakage problem

    Why it's wrong here

    Train/test leakage usually causes inflated performance on both classes, not just one.

  • The feature extraction is incorrect for the minority vendor invoices

    Why it's wrong here

    While possible, the most common and likely cause is class imbalance, especially when the overall F1 is high.

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