AWS Certified AI Practitioner

Domain 1: Fundamentals of Artificial Intelligence and Machine Learning (20%)Core concepts of AI, machine learning (ML), and deep learning. Types of machine learning (supervised, unsupervised, reinforcement learning). Data types, preparation, and basic ML evaluation metrics. Real-world AI and ML use cases and problem-solving. Domain 2: Fundamentals of Generative AI (24%)Concepts behind generative AI, Large Language Models (LLMs), and foundation models (FMs). Prompt engineering basics and contextual learning. Hallucinations, bias, and limitations of generative AI models. Core terminology (tokens, fine-tuning, RAG / Retrieval-Augmented Generation). Domain 3: Applications of Foundation Models (28%)AWS AI and ML services (e.g., Amazon Bedrock, Amazon SageMaker, Amazon Comprehend, Amazon Polly, Amazon Lex, Amazon Transcribe). Choosing the right AWS tool for specific business requirements. Deploying, managing, and optimizing foundation model applications on AWS. Domain 4: Guidelines for Responsible AI (14%)Core principles of responsible AI (fairness, explainability, transparency, privacy). Identifying and mitigating bias and toxicity in AI models.AWS tools and guardrails for safe AI deployment. Domain 5: Security, Compliance, and Governance for AI Solutions (14%)Data privacy, data protection, and intellectual property considerations in AI.AWS security and governance services (e.g., AWS IAM, Amazon Macie, AWS Config). Regulatory and compliance frameworks relevant to artificial intelligence.