AWS Certified AI Practitioner Study Guide

Preface

• What Does This Course Cover?
• Who Should Read This Course

Basic AI Concepts and Terminology

• A Brief History of AI
• Diving Deeper into Terms You Should Know
• The Relationship Among AI, ML, and Deep Learning
• Understanding Data Types in AI Models
• Making Predictions Using Trained Models
• Summary
• Exam Essentials

Basic Concepts of Generative AI

• A New Way to Interact with AI
• From Text to Numbers: Tokens, Chunking, and Embeddings
• The Transformer Architecture and Foundation Models
• Beyond Text: Multi-modal Models
• Prompt Engineering
• The Upsides and Downsides of Gen AI
• Summary
• Exam Essentials

Applications of AI and ML in Real-World Use Cases

• Key Trends in AI and ML Applications
• Use Cases Unsuitable for AI and ML Applications
• Choosing the Right ML Techniques for Different Use Cases
• Summary
• Exam Essentials

AWS AI and ML Services

• An Overview of AWS Managed AI and ML Services
• AWS AI Services
• AWS ML Services
• Summary
• Exam Essentials

Model Selection and Prompt Engineering

• Selecting the Right Foundation Model for Your Use Case
• The Effect of Inference Parameters on Model Responses
• Prompt Engineering
• Summary
• Exam Essentials

Generative AI Applications with RAG and Agents

• Retrieval-Augmented Generation Workflow
• Amazon Bedrock Knowledge Bases
• Amazon Bedrock Agents
• Summary
• Exam Essentials

Model Customization and Evaluation

• Overview of Customization Techniques
• Pre-training Models: Building the Foundation
• Fine-tuning
• AWS Services for Pre-training and Fine-tuning
• Data Processing
• Model Evaluation
• Summary
• Exam Essentials

MLOps

• MLOps Phases
• MLOps Pipeline
• Automating MLOps
• SageMaker Inference
• Inference Optimizations for Large Language Models
• Summary
• Exam Essentials

Implementing Responsible AI with AWS Services

• Key Principles of Responsible AI
• ML Governance with SageMaker AI
• Amazon SageMaker Clarify
• Amazon Bedrock Guardrails
• Amazon Bedrock Evaluations
• Summary
• Exam Essentials

AI Security, Governance, and Compliance

• Security of AI Systems
• Data Governance Strategies
• Compliance and Regulatory Frameworks in AI
• Summary
• Exam Essentials

Practice Test 1 

Practice Test 2

• Question

Flashcard

• Flashcard