Study Guide
AWS Certified AI Practitioner Study Guide
Use the official AWS domain outline to connect AI and machine learning fundamentals, generative AI, foundation model applications, responsible AI, and AWS security governance to scenario-based questions and explanations.
How the Exam Is Structured
AWS Certified AI Practitioner (AIF-C01) validates AI and machine learning fundamentals, generative AI, foundation model applications, responsible AI, and AWS security governance. The ExamPal practice bank includes 400 premium questions and 40 free questions mapped across the official blueprint.
| Domain | Weight | Focus |
|---|---|---|
| Domain 1: Fundamentals of AI and ML | 20% | Task 1.1: Explain basic AI concepts and terminologies; Define basic AI terms |
| Domain 2: Fundamentals of Generative AI | 24% | Sources of FM models; Task 2.1: Explain the basic concepts of generative AI (GenAI) |
| Domain 3: Applications of Foundation Models | 28% | FM lifecycle; Context engineering |
| Domain 4: Guidelines for Responsible AI | 14% | 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 |
| Domain 5: Security, Compliance, and Governance for AI Solutions | 14% | 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) |
20% of exam
Domain 1: Fundamentals of AI and ML
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.
24% of exam
Domain 2: Fundamentals of Generative AI
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.
28% of exam
Domain 3: Applications of Foundation Models
Design considerations, prompt engineering, training/fine-tuning, and methods to evaluate foundation model performance.
14% of exam
Domain 4: Guidelines for Responsible AI
Develop responsible AI systems; recognize the importance of transparent and explainable models.
14% of exam
Domain 5: Security, Compliance, and Governance for AI Solutions
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.
Key Terms to Know
These terms are loaded from the shared terminology pack and appear across the question explanations.
- 200,000-token context windows
- The default context window size supported by Claude Opus and Sonnet according to the text.
- A2I
- Abbreviation for Amazon Augmented AI.
- AI Governance Protocol
- A repeating cycle that connects policies, frameworks, reviews, transparency standards, and training into an AI governance program. It is described as addressing distinct accountability gaps and helping the program withstand external scrutiny and remain effective as AI systems evolve.
- AI governance
- The governance layer that the compliance services in the text support for AI workloads.
- AI literacy training
- Annual training required for all employees that covers what AI is, how the organization's AI-use policy applies to their work, and what to do when they encounter an AI output they suspect is incorrect or harmful.
- AI/ML lifecycle
- The end-to-end lifecycle of AI and machine learning described in the text as moving data from collection through training, deployment, and monitoring, with AWS services participating at each stage.
- AI/ML pipeline
- The sequence of steps a team executes to go from raw data to a working model. Each stage receives an artifact from the previous step, transforms it, and passes the result forward.
- AIF-C01
- The exam guide version referenced in the note about MemoryDB being removed from the vector storage options in version 1.1.
- ANN
- The acronym for approximate nearest-neighbor, an algorithm type used to search embeddings quickly in vector databases.
- API
- An interface through which Amazon Q Business exposes the assistant without requiring infrastructure management.
- API audit logging
- The recording of API activity, as done by AWS CloudTrail, to prove who did what and when.
- API-level prompt separation
- A mitigation for Hijacking / Injection that keeps system instructions separate from user content at the API level.
- APIs
- The interface through which AWS managed AI services provide pre-trained capabilities.
- ARPU
- Average revenue per user.
- AUC
- A metric that the AIF-C01 v1.1 exam guide replaced with precision and recall in the v1.1 list; the text does not define it further.
- AWS
- The cloud platform that provides services such as Amazon Rekognition, Amazon Comprehend, Amazon Translate, Amazon Transcribe, Amazon Bedrock, Amazon SageMaker AI, and Amazon SageMaker Clarify.
- AWS AI Services
- A chain of AWS services used in the conceptual voice channel flow to process voice input, transcribe the audio, interpret intent, and synthesize a spoken reply. The text specifically describes the handoff sequence as Transcribe to Lex to Comprehend to Polly.
- AWS AI/ML service
- An AWS service included in the text as an example of artificial intelligence.
Official Materials and Guidance
This page is built from AWS AIF-C01 official exam guide, the shared syllabus, topic tree, terminology pack, free pack, and premium pack.
- -AWS Aif c01 Exam Guide