AIF-C01 Question #177: Real Exam Question with Answer & Explanation

Question

An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models. Which solution should the ML team use when publishing the custom ML models?

Options

• ACreate documents with the relevant information. Store the documents in Amazon S3.
• BUse AWS A] Service Cards for transparency and understanding models.
• CCreate Amazon SageMaker Model Cards with Intended uses and training and inference details.
• DCreate model training scripts. Commit the model training scripts to a Git repository.

Explanation

The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models. Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations.

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