AWS Certified AI Practitioner · Free Practice Question Medium
Question 26
A healthcare company is using machine learning models in Amazon SageMaker to predict patient outcomes based on various health indicators. To comply with regulatory requirements and build trust with medical professionals, the company needs to understand and explain how different input features, such as age, blood pressure, and medical history, contribute to the model’s predictions. The company is exploring which Amazon SageMaker service can provide this level of transparency and interpretability for their machine learning models.
Which Amazon SageMaker service will help the company understand how an input feature contributes to the predictions of a machine learning model?
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A
Amazon SageMaker JumpStart
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B
Amazon SageMaker Ground Truth
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C
Amazon SageMaker Clarify
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D
Amazon SageMaker Canvas
Reveal correct answer
Correct answer: C
Explanation
Correct option:
Amazon SageMaker Clarify
SageMaker Clarify automatically evaluates foundation models for your generative AI use case with metrics such as accuracy, robustness, and toxicity to support your responsible AI initiative. SageMaker Clarify explains how input features contribute to your model predictions during model development and inference. Evaluate your FM during customization using automatic and human-based evaluations.
SageMaker Clarify is integrated with SageMaker Experiments to provide a feature importance graph detailing the importance of each input for your model’s overall decision-making process after the model has been trained. These details can help determine if a particular model input has more influence than it should on overall model behavior. SageMaker Clarify also makes explanations for individual predictions available through an API.
Incorrect options:
Amazon SageMaker Ground Truth - Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities, allowing you to harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. You can complete a variety of human-in-the-loop tasks with SageMaker Ground Truth, from data generation and annotation to model review, customization, and evaluation, either through a self-service or an AWS-managed offering.
Amazon SageMaker Canvas - SageMaker Canvas offers a no-code interface that can be used to create highly accurate machine-learning models - without any machine-learning experience or writing a single line of code. SageMaker Canvas provides access to ready-to-use models including foundation models from Amazon Bedrock or Amazon SageMaker JumpStart or you can build your own custom ML model using AutoML powered by SageMaker AutoPilot.
Amazon SageMaker JumpStart - Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can evaluate, compare, and select Foundation Models (FMs) quickly based on pre-defined quality and responsibility metrics to perform tasks like article summarization and image generation. Pretrained models are fully customizable for your use case with your data, and you can easily deploy them into production with the user interface or SDK.
Reference:
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