AWS Certified AI Practitioner AIF-C01 Practice Question
A team finds its sentiment analysis training set contains 90% positive and 10% negative labels. Which risk does this class imbalance most likely create if they train the model without corrections?
AWS services will block training when class ratios exceed a predefined threshold such as 4:1.
The algorithm will automatically balance the classes during training, so no additional action is needed.
Training will take significantly longer because the optimizer must process more epochs to converge.
The model may appear accurate overall but will likely misclassify many negative examples.
When one class dominates the training data, many algorithms learn decision boundaries that favor that majority class. The model can achieve high overall accuracy simply by predicting the majority class, yet it will perform poorly on the minority class. This results in misleading aggregate metrics and unfair treatment of the under-represented class. Longer training time, automatic re-balancing, or a policy block from AWS are not typical consequences of class imbalance.
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Why does class imbalance affect model performance?
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Do AWS machine learning services automatically handle class imbalance?
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Why does class imbalance affect model accuracy?
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How can class imbalance be addressed during training?
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What are some consequences of ignoring class imbalance in sentiment analysis?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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