AWS Certified AI Practitioner

Core AI/ML concepts, terminology, the ML lifecycle, and when (and when not) to use machine learning.

• Explain basic AI concepts and terminologies
• Identify practical use cases for AI
• Describe the ML development lifecycle

Foundation models, tokens, embeddings, prompt engineering basics, and the capabilities and limitations of generative AI.

• Explain the basic concepts of generative AI
• Understand the capabilities and limitations of generative AI for business
• Describe AWS infrastructure and technologies for building generative AI applications

Designing applications with foundation models: prompt engineering, retrieval-augmented generation, fine-tuning, and evaluation.

• Describe design considerations for foundation-model applications
• Choose effective prompt-engineering techniques
• Describe training and fine-tuning of foundation models
• Describe methods to evaluate foundation-model performance

Responsible AI dimensions: fairness, bias, transparency, explainability, safety, and how to develop responsibly.

• Explain the development of AI systems that are responsible
• Recognize the importance of transparent and explainable models

Securing AI systems, data governance, compliance standards, and the AWS shared responsibility model applied to AI.

• Explain methods to secure AI systems
• Recognize governance and compliance regulations for AI systems