AWS Certified AI Practitioner Exam Prep

Covers the fundamentals of AI and ML and represents 20% of the scored content on the exam. This domain focuses on basic AI concepts, practical use cases, and the AI/ML development lifecycle.

• Task 1.1: Explain basic AI concepts and terminologies
• Define basic AI terms
• AI, ML, GenAI, deep learning, agentic AI
• Task 1.2: Identify practical use cases for AI
• Applications where AI/ML can provide value
• When AI/ML solutions are not appropriate
• Task 1.3: Describe the AI/ML development lifecycle

Key references: AWS AIF-C01 official exam guide · ExamPal shared topic tree

Covers the fundamentals of generative AI and represents 24% of the scored content on the exam. This domain focuses on GenAI concepts, business capabilities and limitations, and AWS infrastructure and technologies for building GenAI applications.

• Sources of FM models
• Task 2.1: Explain the basic concepts of generative AI (GenAI)
• Foundational GenAI concepts
• Potential use cases for GenAI models

Key references: AWS AIF-C01 official exam guide · ExamPal shared topic tree

Design considerations, prompt engineering, training/fine-tuning, and methods to evaluate foundation model performance.

• FM lifecycle
• Context engineering

Key references: AWS AIF-C01 official exam guide · ExamPal shared topic tree

Develop responsible AI systems; recognize the importance of transparent and explainable models.

• Task 4.2: Recognize the importance of transparent and explainable models
• Describe the differences between models that are transparent and explainable and models that are not transparent and explainable
• Describe tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, SageMaker Clarify, Amazon Bedrock Model Evaluations, open source models, data, licensing)

Key references: AWS AIF-C01 official exam guide · ExamPal shared topic tree

Domain 5 covers security, compliance, and governance for AI solutions and represents 14% of the scored content on the exam. It emphasizes securing AI systems, managing data and model risks, and following governance and compliance practices using AWS services and organizational controls.

• Task 5.1: Explain methods to secure AI systems
• Identify AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model; Amazon Bedrock AgentCore Identity; Policy in AgentCore; Amazon Bedrock Guardrails)
• Describe the concept of source citation and documenting data origins (for example, data lineage, data cataloging, Amazon SageMaker Model Cards)
• Task 5.2: Recognize governance and compliance regulations for AI systems
• Identify AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor)
• Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention)

Key references: AWS AIF-C01 official exam guide · ExamPal shared topic tree