AWS Certified AI Practitioner AIF-C01 Practice Question
An AWS team must pick a model for a fraud-detection project that will be reviewed by regulators. They can choose a simple linear model or a deep neural network. Which tradeoff between interpretability and model performance should the team expect?
Higher interpretability and higher accuracy when the team selects the deep neural network.
Greater interpretability with the linear model but potentially lower predictive accuracy compared to the neural network.
Transparent models typically have slower inference because explanations must be generated after each prediction.
Transparent models usually demand significantly more training data to remain interpretable.
Simple, transparent models such as linear or logistic regression expose how each input contributes to the prediction, making them easy for auditors to understand. However, these models usually cannot capture complex patterns as well as deep neural networks, so accuracy can be lower. Deep neural networks often achieve higher predictive performance but are considered "black-box" and provide little built-in interpretability. The other options describe consequences that are not generally linked to transparency (such as needing more data, slower inference, or increased interpretability with deep models).
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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