AWS Certified AI Practitioner AIF-C01 Practice Question
A company subject to strict regulatory audits can choose between a logistic regression model and a large transformer model. The transformer is 5% more accurate on test data. Which trade-off should the team recognize when deciding on model safety versus transparency?
The transformer provides both the highest accuracy and the highest transparency, so it is the safest choice.
The transformer achieves interpretability without extra tools, so post-hoc explanations are not required.
The logistic regression offers greater interpretability, whereas the transformer delivers higher accuracy at the cost of reduced transparency.
Using a transparent model like logistic regression eliminates all model safety concerns, making further governance unnecessary.
Logistic regression is considered a transparent or "white-box" algorithm, so auditors can easily trace how each input contributes to a prediction. Deep transformer networks are typically "black-box" models: they often provide higher predictive performance but their internal reasoning is difficult to explain without additional tools. Therefore, selecting the transformer improves raw accuracy but reduces interpretability-potentially lowering transparency and making safety validation harder. Choosing the logistic regression maintains explainability but may sacrifice some performance. The other statements are incorrect because
transformers are not inherently more transparent,
transparency does not remove all safety risks, and
transformers usually still require post-hoc explanation methods to be interpretable.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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