AWS published version 1.1 of the AIF-C01 exam guide on April 30, 2026, updating the guide from March 26, 2026. The revision makes agentic AI and practical foundation-model work more explicit, including model selection, token-based inference costs, context and prompt management, business measures, and hallucination detection and grounding. It also changes the guide’s in-scope and out-of-scope service lists.
What changed in AIF-C01 version 1.1?
The main shift is toward applying generative AI and foundation models in real systems, with agentic AI now named more directly in the objectives. AWS also revised several existing descriptions and examples, so version 1.1 is not just a longer list of new terms.
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AI, ML, and pipeline fundamentals
Version 1.1 adds generative AI and agentic AI to basic AI terminology, and agentic AI to the comparison among AI, machine learning, generative AI, and deep learning. Inference examples now include asynchronous and serverless inference alongside batch and real-time inference. The learning objective is reframed to cover different AI/ML learning types, with supervised, unsupervised, and reinforcement learning as examples.
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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. Examples in the guide include Amazon Bedrock, Amazon Q, Amazon Quick, Kiro, and SageMaker AI.
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Foundation-model choices and operating trade-offs
The guide now explicitly asks candidates to choose between traditional machine-learning models and foundation models for a use case, taking account of constraints such as regulation, explainability, and operations. It also adds token-based pricing and its effect on inference cost and performance. This connects model choice to deployment realities: candidates should be ready to reason about both what a model can do and the practical limits of using it.
Context, prompts, agents, and output quality
New objectives cover context engineering in foundation-model applications and prompt versioning and management strategies using Amazon Bedrock Prompt Management. Agentic AI coverage includes multi-agent patterns, how MCP connects agents to external systems, communication between agents, memory, tool use, and workflow orchestration.
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Version 1.1 also names measures for aligning AI solutions with business objectives, including task completion rate, user satisfaction, and cost per interaction. For hallucination detection and grounding, examples include RAG grounding, output validation, and confidence scoring.
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AWS revised both scope lists. The two lists are separate: removal from the out-of-scope list does not by itself establish that a service was added to the in-scope list. AWS describes the lists as non-exhaustive.
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| Change | Services and features |
|---|---|
| Added to the in-scope list | Amazon Aurora; Amazon Bedrock AgentCore; Kiro; Strands Agents; Amazon Q; Amazon SageMaker JumpStart; AWS Transform |
| Removed from the in-scope list | Amazon MemoryDB |
| Removed from the out-of-scope list | AWS DeepComposer; Amazon FinSpace; Amazon Honeycode; AWS IAM Identity Center; AWS Marketplace; AWS Organizations; Amazon WorkDocs |
These are changes to the guide’s lists, not a promise that each named service will appear in a specific exam question. AWS says the exam guide is not a comprehensive inventory of exam content.
When do the changes affect the exam?
AWS’s revisions page says: “Exam guide updates will be published approximately one month before updates will be reflected on your exam.” The word “approximately” matters: the statement does not establish the exact transition date for an individual exam appointment. Check the current guide and the information associated with your scheduled exam rather than inferring an exact cutoff from the publication date.
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| Guide version | Publication date |
|---|---|
| Version 1.0 | March 26, 2026 |
| Version 1.1 | April 30, 2026 |
How should candidates adjust their preparation?
Use the guide version relevant to your exam and compare its objectives with what you have already studied. For version 1.1, give deliberate attention to the newly explicit applied topics rather than memorizing service names alone.
- Practice deciding when a traditional ML model or a foundation model better fits a use case, including regulatory, explainability, and operational constraints.
- Understand how token-based pricing relates to inference cost and performance.
- Be able to explain context engineering, prompt versioning and management, and the main components of agentic and multi-agent systems.
- Connect solution measures such as task completion, user satisfaction, and cost per interaction to business objectives.
- Know approaches for detecting or reducing hallucinations, including grounding with RAG, output validation, and confidence scoring.
- Review the revised pipeline examples and service lists, while treating them as guide examples rather than guaranteed question topics.
What the guide says about the exam
The current AIF-C01 guide assigns scored-content weights of 20% to Fundamentals of AI and ML, 24% to Fundamentals of GenAI, 28% to Applications of Foundation Models, 14% to Guidelines for Responsible AI, and 14% to Security, Compliance, and Governance for AI Solutions. These are domain weights, not guaranteed question counts.
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AWS describes the exam as having 50 scored questions and 15 unscored questions, with a minimum passing scaled score of 700 on a scale from 100 to 1,000. The guide’s target candidate has up to six months of exposure to AI/ML on AWS and uses, but does not necessarily build, AWS AI/ML solutions.
Read the AWS AIF-C01 revisions page for version history and the current AWS Certified AI Practitioner exam guide for objectives, domain weights, and exam details. AWS notes that the guide is not a comprehensive list of all exam content.
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