AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01Updated 8d ago · Aug 28, 2026

AWS Certified AI Practitioner AIF-C01

453 questions across 1 topics, with suggested answers, explanations where available, and imported community discussion. The first 45 questions are free to preview.

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#	Topic	Questions	Free
1	Security, Compliance, and Governance for AI Solutions	453	45

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Question 1 · Security, Compliance, and Governance for AI Solutions Open

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: C Amazon SageMaker Model Cards provide a standardized, structured format to document model details including intended use cases, training methodology, performance metrics, evaluation results, ethical considerations, and version history. This facilitates comprehensive auditing, transparency, and accountability when sharing models across teams. Other options lack the standardization and audit-specific features needed for model governance. **Learn more:** [Amazon SageMaker Model Cards](https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html)

Question 2 · Security, Compliance, and Governance for AI Solutions Open

A company has deployed an AI application to production on AWS, and the application's responses have grown less accurate over time. The company requires a solution that sends alerts when application performance drifts. Which AWS service or feature meets this requirement?

Answer: B Amazon SageMaker Model Monitor continuously monitors ML models in production, including model-quality metrics such as accuracy. It detects deviations from configured baselines or thresholds and supports alerts when model performance drifts. **Learn more:** [Data and model quality monitoring with Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) · [Model quality metrics and Amazon CloudWatch monitoring](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-metrics.html)

Question 3 · Fundamentals of AI and ML Open

A company uses Amazon SageMaker to deploy a model that determines whether social media posts contain specific topics. The company must demonstrate how different input features affect the model's behavior. Which SageMaker feature satisfies these requirements?

Answer: B Amazon SageMaker Clarify provides model explainability through feature attribution, showing how individual input features contribute to model predictions. It also supports NLP explainability for text features. **Learn more:** [Model Explainability - Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-explainability.html) · [ClarifyTextConfig - Amazon SageMaker API Reference](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ClarifyTextConfig.html)