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AWS Certified AI Practitioner AIF-C01 Practice Question

A team trains an image-classification model that scores 99% accuracy on the training data but only 60% on a newly collected test set, with especially poor predictions for certain demographic groups. Which condition most likely explains this behavior?

  • High variance that causes the model to overfit the training data

  • A fully balanced training dataset with equal representation

  • High bias that leads to underfitting across all datasets

  • Low model complexity that prevents the model from capturing patterns

AWS Certified AI Practitioner AIF-C01
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