AWS Certified AI Practitioner
Understand AI, ML, and generative AI on AWS
Go from AI-curious to AI-confident. Learn machine learning fundamentals, generative AI, foundation models, and responsible AI — then prove it by passing the AIF-C01 exam.
The AIF-C01 exam at a glance
What the exam tests
AIF-C01 is scored across 5 domains. Our learning path covers each one in proportion to its exam weight.
Domain 1: Fundamentals of AI and ML
20%Core AI, machine learning, and deep learning concepts — terminology, ML lifecycle, and the AWS AI/ML service stack.
Domain 2: Fundamentals of Generative AI
24%Foundation models, LLMs, tokens and embeddings, prompt basics, and AWS generative AI services like Amazon Bedrock.
Domain 3: Applications of Foundation Models
28%Choosing and customizing foundation models: prompt engineering, RAG, fine-tuning, agents, and model evaluation.
Domain 4: Guidelines for Responsible AI
14%Fairness, bias, explainability, transparency, and the AWS tools that support responsible AI development.
Domain 5: Security, Compliance, and Governance for AI
14%Securing AI systems: IAM, data protection, guardrails, and governance and compliance frameworks for AI workloads.
Your learning path
Work through the modules in order — each lesson ends with a short knowledge check, and the path finishes with full-length mock exams.
- 1
Getting Started
What the AI Practitioner certification covers, who it's for, and exactly how the AIF-C01 exam works.
- 2
AI & ML Fundamentals
Fundamentals of AI and MLAI vs ML vs deep learning, how models learn, how they're evaluated, and the AWS AI/ML service stack.
- 9 minAI, ML & Deep LearningThe nested definitions of AI, machine learning, and deep learning — plus the three ways machines learn.
- 9 minThe ML Lifecycle & DataFrom business problem to production model: the ML pipeline stages, data splits, and MLOps basics.
- 9 minEvaluating ML ModelsOverfitting vs underfitting, accuracy vs precision vs recall, and the metrics vocabulary the exam tests.
- 10 minThe AWS AI/ML Service StackSageMaker, the pre-trained AI services, and how to choose between building, buying, and prompting.
- 3
Generative AI Fundamentals
Fundamentals of Generative AIFoundation models, LLMs, tokens and embeddings, generative AI's strengths and pitfalls, and AWS's GenAI services.
- 10 minFoundation Models & LLMsWhat makes generative AI different, how transformers and tokens work, and the vocabulary of foundation models.
- 9 minGenAI: Capabilities, Limits & Use CasesWhere generative AI shines, where it fails (hallucinations and friends), and how to pick the right use case.
- 9 minAWS Generative AI ServicesAmazon Bedrock, Amazon Q, PartyRock, and SageMaker — the AWS GenAI lineup and when to use each.
- 8 minInference Parameters & GenAI PricingTemperature, top-p, max tokens — plus how generative AI is priced and the trade-offs that drive model choice.
- 4
Applying Foundation Models
Applications of Foundation ModelsPrompt engineering, RAG, fine-tuning, agents, and model evaluation — the exam's biggest domain, all about adapting FMs to real problems.
- 10 minPrompt EngineeringZero-shot to chain-of-thought, the anatomy of a great prompt, and the attacks every practitioner must recognize.
- 9 minRAG & Knowledge BasesRetrieval-augmented generation: how it works, why it beats fine-tuning for company knowledge, and vector databases on AWS.
- 9 minCustomizing Foundation ModelsThe adaptation ladder — prompting → RAG → fine-tuning → pre-training — and how to pick a model and method.
- 8 minAgents & GenAI Application ArchitectureBedrock Agents, tool use, and how the pieces — models, knowledge bases, guardrails — assemble into applications.
- 8 minEvaluating GenAI ApplicationsROUGE, BLEU, and BERTScore; human evaluation and benchmarks; and tying model quality to business results.
- 5
Responsible AI
Guidelines for Responsible AIFairness, bias, explainability, and the AWS tools — Clarify, Guardrails, A2I, model cards — that make AI trustworthy.
- 9 minPrinciples of Responsible AIThe dimensions of trustworthy AI — fairness, explainability, transparency, privacy, robustness — and where bias comes from.
- 8 minResponsible AI Tools on AWSSageMaker Clarify, Model Monitor, Augmented AI, Bedrock Guardrails, and the documentation artifacts — model cards and service cards.
- 7 minLegal & Ethical ConsiderationsIP and copyright risks, data privacy, regulatory exposure, and the practices that keep GenAI adoption defensible.
- 6
Security & Governance for AI
Security, Compliance, and Governance for AISecuring AI workloads with IAM, encryption, and private networking — plus the data governance and compliance layer.
- 9 minSecuring AI SystemsThe AWS security toolkit applied to AI: IAM, KMS, PrivateLink, S3 protection, and the shared responsibility model.
- 8 minData Governance for AIQuality, lineage, lifecycle, and access — governing the data that makes or breaks every model.
- 8 minCompliance & Governance for AI WorkloadsArtifact, Config, Audit Manager, and CloudTrail applied to AI — plus the governance habits regulators expect.
- 7
Exam Readiness
The rapid-review cheat sheet and exam-day strategy — then prove it in two full-length mock exams.
AI Practitioner FAQ
Who should take the AWS Certified AI Practitioner exam?
Anyone who works with — or wants to work with — AI on AWS without needing to build models from scratch: business analysts, product managers, developers, marketers, and students. It validates conceptual knowledge of AI, ML, and generative AI plus the AWS services around them.
Do I need coding or math skills for AIF-C01?
No. The exam is conceptual. You will not write code or derive equations. You need to understand what techniques like fine-tuning, RAG, and prompt engineering do, when to use each, and which AWS service fits a scenario.
Should I take Cloud Practitioner before AI Practitioner?
It is not required, but it helps. AIF-C01 assumes light familiarity with core AWS concepts like IAM, S3, and the shared responsibility model. If you are completely new to AWS, our Cloud Practitioner path first is the gentler route.
What is the passing score for AIF-C01?
700 on a scaled score of 100–1000. The exam has 65 questions (50 scored, 15 unscored) and you get 90 minutes.
How current is the exam content?
AIF-C01 focuses heavily on generative AI — Amazon Bedrock, foundation models, prompt engineering, RAG, and responsible AI. Our lessons and question pool are aligned to the official AIF-C01 exam guide domains and weightings.