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AWS published AIF-C01 exam guide version 1.1 on April 30, 2026, replacing version 1.0 from March 26, 2026. The revision makes generative AI and agentic AI more explicit, adds practical skills for choosing and operating foundation-model applications, and updates the lists of in-scope and out-of-scope AWS services. The guide updates are said to appear on exams approximately one month after publication—not on a guaranteed date for every appointment.
What changed in the AIF-C01 objectives?
Version 1.1 expands the guide beyond foundational AI and machine-learning terminology toward decisions and operating practices involved in applying generative AI and foundation models. AWS’s revision notes identify both revised objectives and newly added ones; they do not mean every listed example is guaranteed to appear as an individual exam question. AWS also says the guide is not a comprehensive list of exam content.
AI, machine learning, and pipeline fundamentals
The basic terminology now includes generative AI (GenAI) and agentic AI, and the comparison among AI, ML, GenAI, and deep learning now includes agentic AI. Inference examples expand beyond batch and real-time to include asynchronous and serverless inference. The learning objective is rephrased to cover different types of AI/ML learning, with supervised, unsupervised, and reinforcement learning as examples.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Real-world application examples now include knowledge bases and agentic AI. Pipeline objectives ask candidates to describe and differentiate AI/ML pipeline components and identify services used at pipeline stages. Version 1.1 gives Amazon Bedrock, Amazon Q, Amazon Quick, Kiro, and SageMaker AI as examples.
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Foundation-model selection and operating costs
The guide now explicitly asks candidates to choose between traditional ML models and foundation models for a use case, accounting for regulatory, explainability, and operational constraints. It also adds how token-based pricing affects inference cost and performance. This connects model selection to both the requirements of a task and the practical cost and behavior of serving a model.
Context, prompts, and agentic AI
Context engineering in foundation-model applications is now an objective. Prompt versioning and management strategies are also explicit, with Amazon Bedrock Prompt Management named as an example. Candidates should recognize agentic AI concepts that the guide lists, including multi-agent patterns and communication, memory, tool use, workflow orchestration, and MCP’s role in connecting agents to external systems.
Measurement, hallucinations, and responsible use
Version 1.1 adds measures that align AI solutions with business objectives, including task completion rate, user satisfaction, and cost per interaction. It also names approaches to hallucination detection and grounding: retrieval-augmented generation (RAG) grounding, output validation, and confidence scoring. These additions sit alongside the guide’s broader responsible-AI, security, compliance, and governance coverage; they are stated objectives, not a promise that a particular technique will be tested in a particular question.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhich AWS services changed scope?
The guide maintains separate in-scope and out-of-scope lists. AWS describes these lists as non-exhaustive, so a service’s removal from one list should not be treated as proof that it was added to the other.
| Scope list | Version 1.1 change |
|---|---|
| Added to in-scope | Amazon Aurora; Amazon Bedrock AgentCore; Kiro; Strands Agents; Amazon Q; Amazon SageMaker JumpStart; AWS Transform |
| Removed from in-scope | Amazon MemoryDB |
| Removed from out-of-scope | AWS DeepComposer; Amazon FinSpace; Amazon Honeycode; AWS IAM Identity Center; AWS Marketplace; AWS Organizations; Amazon WorkDocs |
In particular, the seven services removed from the out-of-scope list are not thereby confirmed as in-scope. Check the current guide’s separate lists rather than inferring status from a removal alone.
When do the guide changes apply to the exam?
AWS’s revision page says: “Exam guide updates will be published approximately one month before updates will be reflected on your exam.” The word “approximately” matters: this statement does not establish an exact transition date for an individual exam appointment. Check the current official guide and AWS exam information when planning which objectives to study.
How should candidates adjust their preparation?
- Confirm the guide version. Compare your study plan with the version AWS lists for your exam preparation, rather than assuming material based on version 1.0 covers every version 1.1 objective.
- Prioritize the newly explicit application skills. Review agentic AI, foundation-model versus traditional-ML selection, token-based inference pricing, context engineering, prompt management, business-aligned measures, and hallucination detection and grounding.
- Review the revised fundamentals and scope lists. Include the changed AI/ML terminology, inference examples, pipeline objectives, and service-list updates without treating the examples or lists as exhaustive.
- Use the domain weights to allocate study time, not predict question counts. The weights apply to scored content and do not guarantee a particular number of questions in any domain.
What exam details remain useful?
The version 1.1 exam guide describes 50 scored questions and 15 unscored questions. It sets the minimum passing score at 700 on a scaled range of 100–1,000. The intended candidate has up to six months of exposure to AI/ML on AWS and uses, but does not necessarily build, AWS AI/ML solutions.
| Scored-content domain | Weight |
|---|---|
| Fundamentals of AI and ML | 20% |
| Fundamentals of GenAI | 24% |
| Applications of Foundation Models | 28% |
| Guidelines for Responsible AI | 14% |
| Security, Compliance, and Governance for AI Solutions | 14% |
These are the guide’s scored-domain weights, not a guaranteed breakdown of question counts.
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Official AWS references
- AWS Certified AI Practitioner revisions — revision history and AWS’s approximate timing statement.
- AWS Certified AI Practitioner Exam Guide (AIF-C01) — objectives, exam structure, domain weights, candidate profile, and scope lists.
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