AWS Certified AI PractitionerStudy guide
The foundational certification for AI and generative AI on AWS (AIF-C01).
About AWS Certified AI Practitioner (AIF-C01)
AWS Certified AI Practitioner (AIF-C01) is a Fundamentals-level certification from AWS. This page organizes the exam scope into a 4-chapter, 10-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Fundamentals of AI and ML~20%
- Fundamentals of generative AI~24%
- Applications of foundation models~28%
- Guidelines for responsible AI~14%
- Security, compliance, and governance for AI~14%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://aws.amazon.com/certification/certified-ai-practitioner/
1Fundamentals of AI and Machine Learning
- 1.1AI, Machine Learning, and Deep Learning
Understand how AI, machine learning (ML), and deep learning relate, and their basic concepts. The starting point for AIF-C01.
- 1.2ML Learning Styles and AWS AI/ML Services
Understand the three learning styles—supervised, unsupervised, reinforcement—and where AWS AI/ML services like Amazon SageMaker fit.
2Fundamentals of Generative AI
- 2.1Fundamentals of Generative AI and Foundation Models
Understand the basics of generative AI: large language models (LLMs), tokens, prompts and completions, and foundation models.
- 2.2AWS Generative AI Services
Understand AWS services for generative AI—Amazon Bedrock, Amazon Q, and Amazon SageMaker JumpStart—and when to use each.
3Applications of Foundation Models
- 3.1Prompt Engineering and Inference Parameters
Understand prompt techniques (zero-shot, few-shot, chain-of-thought) and inference parameters like temperature and context window.
- 3.2RAG and Fine-tuning
Understand two main ways to adapt a foundation model to your needs—RAG (retrieval-augmented generation) and fine-tuning—and when to use each.
- 3.3Selecting and Evaluating Foundation Models
Understand criteria for choosing a foundation model (accuracy, cost, latency), how to evaluate, and risks like hallucination.
4Responsible AI, Security, and Governance
- 4.1Guidelines for Responsible AI
Understand responsible AI dimensions—fairness, explainability, transparency, privacy/safety, robustness—and addressing bias and hallucination.
- 4.2Security for AI Solutions
Understand securing AI solutions with IAM least privilege, data encryption and privacy, and Amazon Bedrock Guardrails.
- 4.3Governance and Compliance for AI
Understand governance for AI use—defining policies, monitoring and logging usage, accountability, and compliance.