AWS AI / AWS Certified AI Practitioner
Comprehensive study guides covering the AWS Certified AI Practitioner (AIF-C01) exam blueprint: fundamentals of AI and machine learning, generative AI concepts, applications of foundation models, responsible AI guidelines, and security, compliance, and governance for AI. Each guide is a WCAG 2.0 AA-compliant static study reference with exam-weight notes and quick-review checklists.
Domain Study Guides
Core AI/ML terminology, the AI–ML–deep learning–GenAI hierarchy, inferencing types, data types and learning paradigms, and the end-to-end ML development lifecycle.
Tokens, embeddings, and foundation models; the FM lifecycle; prompt and context engineering; agentic AI concepts; and the advantages, limitations, and business value of GenAI.
FM selection and inference parameters, RAG and vector stores, prompt engineering techniques and risks, fine-tuning and RLHF, and FM evaluation metrics — the exam's heaviest domain.
The six pillars of responsible AI, bias and variance, AWS tools for bias detection and human review, legal risk categories, and the transparency-vs-performance tradeoff.
Securing AI systems under the shared responsibility model, data lineage and governance, AI-specific threats and mitigations, hallucination grounding, and AWS compliance tooling.