AWS Certified AI Practitioner¶

The AWS Certified AI Practitioner certification validates foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts and AWS AI/ML services.

Quick Links¶

• Fact Sheet - Exam logistics and blueprint
• Practice Plan - Structured study schedule

Study Materials¶

Core Notes¶

• AI/ML Fundamentals - Core concepts and terminology
• Amazon Bedrock - Generative AI foundation models
• Responsible AI on AWS - Ethics, governance, and AWS responsible-AI services

Companion Materials¶

• AWS Machine Learning Engineer Associate (MLA-C01) - The natural deeper-dive cert after AI Practitioner
• Anthropic Claude Certified Developer - Foundations - For production GenAI applications

Exam Focus Areas¶

The AWS Certified AI Practitioner exam covers:

1. Fundamentals of AI and ML (20%)
2. AI/ML concepts and terminology
3. Types of AI and ML models

4. AI/ML workflow and lifecycle

5. Fundamentals of Generative AI (24%)

6. Generative AI concepts and applications
7. Foundation models and prompt engineering

8. Retrieval Augmented Generation (RAG)

9. Applications of Foundation Models (28%)

10. Amazon Bedrock and foundation models
11. Model selection and customization

12. Inference and deployment strategies

13. Guidelines for Responsible AI (14%)

14. AI ethics and bias mitigation
15. Fairness, transparency, and accountability

16. Governance and compliance

17. Security, Compliance, and Governance for AI Solutions (14%)

18. Data privacy and protection
19. AI security best practices
20. Compliance frameworks for AI

Key AI/ML Services Focus¶

Generative AI¶

• Amazon Bedrock: Foundation models from AI21 Labs, Anthropic, Cohere, Meta, Stability AI
• Amazon Q: AI-powered assistant for business
• Amazon CodeWhisperer: AI coding companion

Machine Learning Platform¶

• Amazon SageMaker: End-to-end ML platform
• SageMaker Studio: Integrated development environment
• SageMaker Autopilot: Automated ML

AI Services¶

• Amazon Rekognition: Computer vision
• Amazon Textract: Document analysis
• Amazon Comprehend: Natural language processing
• Amazon Polly: Text-to-speech
• Amazon Transcribe: Speech-to-text
• Amazon Translate: Language translation
• Amazon Lex: Conversational AI
• Amazon Kendra: Intelligent search

Prerequisites & Expectations¶

• Recommended Experience: 6+ months of exposure to AI/ML concepts
• Technical Knowledge: Basic understanding of cloud computing
• Business Context: Understanding of AI/ML use cases and business value
• No Coding Required: Focus on concepts rather than implementation

Success Criteria¶

• Understand fundamental AI/ML concepts and terminology
• Identify appropriate AWS AI/ML services for specific use cases
• Understand generative AI and foundation model concepts
• Recognize responsible AI practices and governance requirements
• Demonstrate knowledge of AI security and compliance considerations