AWS Certified AI Practitioner AIF-C01 Practice Question
A team must choose a model that allows business analysts to understand why each prediction was made. They are comparing linear regression with a deep neural network. Which characteristic of linear regression makes it a transparent model?
The contribution of each input feature can be directly read from its model coefficient.
It achieves higher accuracy by learning hierarchical feature representations automatically.
Its architecture is too complex for users to interpret without specialized tools.
Its parameters are hidden inside millions of weights and biases.
Transparent (white-box) models expose how inputs affect outputs. In linear regression every feature has a numeric coefficient, so analysts can read the direction and strength of each input's contribution to the prediction. Deep neural networks learn many hidden weights, require GPUs, and hide relationships, so they are considered non-transparent or black-box models.
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Why is linear regression considered a transparent model?
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What makes deep neural networks non-transparent?
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How do model coefficients in linear regression help interpret predictions?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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