AWS Certified AI Practitioner · Free Practice Question Medium
Question 46
A healthcare company is deploying AI models using Amazon SageMaker to predict patient outcomes and ensure compliance with healthcare regulations. The data science team wants to document important details about their models, such as performance, bias assessments, and intended use. They are considering using SageMaker model cards for this purpose but also want to understand how AI service cards fit into the broader documentation of their AI services. Understanding the differences between these two tools will help the team select the right one for tracking and managing their AI models.
Given this context, how would you highlight the key differences between SageMaker model cards and AI service cards?
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A
SageMaker model cards are used exclusively for monitoring model performance, whereas AI service cards are used for managing model security
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B
SageMaker model cards provide technical documentation for deploying models, while AI service cards offer transparency about the intended use, limitations, and potential impacts of AWS AI services
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C
SageMaker model cards include information about the model such as intended use and risk rating of a model, training details and metrics, evaluation results, and observations. AI service cards provide transparency about AWS AI services' intended use, limitations, and potential impacts
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D
SageMaker model cards are used to store data for machine learning models, while AI service cards are used for storing user credentials
Reveal correct answer
Correct answer: C
Explanation
Correct option:
SageMaker model cards include information about the model such as intended use and risk rating of a model, training details and metrics, evaluation results, and observations. AI service cards provide transparency about AWS AI services' intended use, limitations, and potential impacts
You can use Amazon SageMaker Model Cards to document critical details about your machine learning (ML) models in a single place for streamlined governance and reporting. You can catalog details such as the intended use and risk rating of a model, training details and metrics, evaluation results and observations, and additional call-outs such as considerations, recommendations, and custom information.
AI Service Cards are a form of responsible AI documentation that provides customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and deployment and performance optimization best practices for AI services from AWS.
Incorrect options:
SageMaker model cards provide technical documentation for deploying models, while AI service cards offer transparency about the intended use, limitations, and potential impacts of AWS AI services - SageMaker model cards do provide detailed information about the models, including performance metrics and compliance, but they are not specifically for deploying models. AI service cards are focused on transparency and understanding the AI services' intended use and limitations.
SageMaker model cards are used exclusively for monitoring model performance, whereas AI service cards are used for managing model security - SageMaker model cards cover a broader range of information including metadata and compliance, not just monitoring. AI service cards are not specifically for managing security.
SageMaker model cards are used to store data for machine learning models, while AI service cards are used for storing user credentials - SageMaker model cards and AI service cards are not used for storing data or user credentials; they are used for providing detailed information about models and AI services, respectively.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html
Discussion
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