AWS Certified AI Practitioner AIF-C01 Practice Question
A startup is training a sentiment analysis model on Amazon SageMaker. The team wants to scan the training data for bias, generate bias metrics again after training, and view feature-attribution explanations such as SHAP values-all before deploying the model. Which AWS service best meets these needs?
Amazon SageMaker Clarify provides both pre-training and post-training bias detection reports and creates explainability reports using SHAP values, making it the correct choice. SageMaker Model Monitor focuses on monitoring data and model drift after deployment, not on pre-training bias analysis. Amazon Augmented AI supplies human review workflows but does not calculate bias or explainability metrics. AWS Trusted Advisor checks an AWS account for cost, security, and performance best practices rather than model bias or explainability.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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