AWS Certified AI Practitioner (AIF-C01) — Question 105

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 build a mechanism that the ML team can use to audit models.

Which solution should the ML team use when publishing the custom ML models?

Answer options

• A. Create documents with the relevant information. Store the documents in Amazon S3.
• B. Use AWS AI Service Cards for transparency and understanding models.
• C. Create Amazon SageMaker Model Cards with intended uses and training and inference details.
• D. Create model training scripts. Commit the model training scripts to a Git repository.

Correct answer: C

Explanation

The correct answer is C, as Amazon SageMaker Model Cards provide a structured way to document the model's intended uses, training processes, and inference details, making it easier for auditing. While option A involves documentation, it lacks the structured approach of Model Cards. Option B focuses on transparency but does not specifically address auditing needs, and option D, while useful for version control, does not provide the necessary model context for auditing.