Volume I Chapter AIF-C01 foundational tier
AI Practitioner
Editor's note — A study companion for the AI Practitioner exam — every domain rebuilt from scratch, with worked practice questions and an exam-grade timed simulation.
65 questions 90 minutes threshold 700/1000 5 domains official guide
Table of Contents
I. Fundamentals Of Ai And Ml 20% weight
AI and ML Core Concepts Supervised, Unsupervised, and Reinforcement Learning Overfitting, Underfitting, Bias, and Variance ML Development Lifecycle Practical AI and ML Use Cases II. Fundamentals Of Generative Ai 24% weight
Generative AI Concepts and Terminology Foundation Models and Large Language Models (LLMs) Tokens, Context Window, and Inference Parameters Embeddings and Vector Databases on AWS Retrieval-Augmented Generation (RAG) GenAI Business Capabilities, Limitations, and AWS Infrastructure III. Applications Of Foundation Models 28% weight
Amazon Bedrock — Model Selection and Pricing Prompt Engineering Techniques Fine-Tuning vs In-Context Learning Amazon SageMaker — Training, Deployment, and MLOps Amazon Q (Business and Developer) and AWS AI Services Foundation Model Evaluation and Benchmarking IV. Guidelines For Responsible Ai 14% weight
Responsible AI Principles and AWS Framework Transparency vs Explainability in AI SageMaker Clarify, Model Monitor, and Amazon A2I V. Security Compliance And Governance For Ai 14% weight
AI Threat Model — Prompt Injection, Jailbreaking, and Adversarial Attacks IAM for AI Workloads and Amazon Bedrock Security Amazon Bedrock Guardrails and Content Controls Data Governance for AI and PII Handling AI Compliance Regulations and Governance Programs