The AWS Certified AI Practitioner (AIF-C01) is the entry-level certification for anyone who wants to prove they understand AI, ML, and generative AI on AWS — without needing to build models. It’s not a developer exam. It’s a foundation-level test for sales, product, project, and support roles, as well as for developers who want a structured overview.
This guide breaks down the official syllabus into its four domains, gives you realistic weightage estimates, and tells you exactly what to study — and what to skip — for each one.
Domain 1: Fundamentals of AI and ML (20%)
This domain tests your baseline understanding of machine learning concepts. You’ll need to know the difference between supervised, unsupervised, and reinforcement learning, and when each is appropriate. Also covered: basic terminology like features, labels, training vs. inference, and overfitting.
Key topics:
- Types of ML: supervised, unsupervised, reinforcement
- Common use cases: regression, classification, clustering, anomaly detection
- The ML pipeline: data collection, training, evaluation, deployment
- Key metrics: accuracy, precision, recall, F1 (conceptual)
Study tip: Don’t dive into math. The exam asks scenario-based questions — e.g., “Which ML type would you use to predict customer churn?” Focus on recognizing patterns, not deriving formulas.
Domain 2: Fundamentals of Generative AI (24%)
This is the largest domain, and it’s where AWS expects you to understand the generative AI stack. You’ll be tested on foundation models (FMs), large language models (LLMs), and the difference between base models and fine-tuned versions. Also covered: prompt engineering basics, RAG (retrieval-augmented generation), and common generative AI use cases.
Key topics:
- Foundation models and their capabilities
- Prompt engineering: zero-shot, few-shot, chain-of-thought
- RAG architecture and when to use it
- Model customization: fine-tuning vs. training from scratch
- Responsible AI: bias, hallucinations, and mitigation
Study tip: Understand why RAG is used — to ground models with up-to-date or proprietary data without retraining. Know the trade-offs between fine-tuning and RAG. The exam loves these comparisons.
Domain 3: Applications of Foundation Models (28%)
This domain is about applying AWS AI services to real problems. You’ll need to know which AWS service to use for a given task: Amazon Textract for OCR, Amazon Transcribe for speech-to-text, Amazon Polly for text-to-speech, Amazon Rekognition for image/video analysis, and Amazon Comprehend for NLP. Also covered: Amazon Bedrock, Amazon SageMaker (at a high level), and AWS AI services like Lex and Kendra.
Key topics:
- AWS AI services: Textract, Transcribe, Polly, Rekognition, Comprehend, Translate, Lex
- Amazon Bedrock: using foundation models via API, model selection, and agents
- Amazon SageMaker: when to use it vs. Bedrock
- Building generative AI applications: chatbots, document summarization, code generation
Study tip: Create a cheat sheet that maps each AWS service to its primary function. The exam gives you a scenario (e.g., “extract text from a scanned PDF”) and you pick the service. That’s the whole domain.
Take a free AWS Certified AI Practitioner demo mock to find out where you stand: Try the demo →
Domain 4: Responsible AI (14%)
This domain covers the ethical and operational aspects of AI. You’ll be tested on fairness, transparency, explainability, privacy, and security. Also included: AWS tools for responsible AI, like SageMaker Clarify, and how to detect bias in training data and model outputs.
Key topics:
- Principles of responsible AI: fairness, accountability, transparency
- Bias detection and mitigation
- Model explainability and interpretability
- Data privacy and security in AI workloads
- AWS services: SageMaker Clarify, Amazon Rekognition Content Moderation, IAM for AI services
Study tip: Focus on the “why” — why responsible AI matters, not the technical implementation. Know what SageMaker Clarify does (detect bias and explain predictions) and when to use it.
Exam Logistics and What to Expect
The AIF-C01 exam is 120 minutes long, with 65 questions (multiple choice and multiple response). You can take it online or at a testing center. The passing score is set by AWS and is not published — but you’ll get a scaled score out of 1000, with a passing threshold typically around 700–720. Verify the exact score and fees on the official AWS portal, as they were last revised in early 2026.
Question types:
- Scenario-based: “A company wants to build a chatbot that answers customer queries using internal documents. Which AWS service should they use?”
- Definition-based: “What is the primary benefit of using RAG?”
- Comparison-based: “What is the difference between Amazon Bedrock and Amazon SageMaker?”
Time management: You have about 1 minute 50 seconds per question. Don’t get stuck — mark and move on. The exam is not adaptive, so you can skip and return.
How to Study for the AIF-C01 in 4 Steps
- 1Learn the AWS AI services — Use the official AWS documentation and free digital training. Build a table of services and use cases.
- 2Understand generative AI fundamentals — Read AWS’s “Generative AI on AWS” whitepaper. Focus on RAG, prompt engineering, and foundation models.
- 3Practice with mock exams — The key is scenario recognition. Take multiple mock tests to get comfortable with the question style.
- 4Review your weak areas — Use the domain breakdown above to prioritize. If you’re weak on responsible AI, spend an extra day on that.
See AWS Certified AI Practitioner mock-test packs and pricing: View plans →
Common Mistakes to Avoid
- Overstudying machine learning math — The exam is conceptual, not mathematical. Don’t waste time on gradient descent.
- Ignoring the “why” behind services — You need to know when to use a service, not just what it does.
- Forgetting about responsible AI — It’s only 14%, but it’s easy points if you understand the principles.
- Not reading the full question — Many questions have two similar answers. Eliminate wrong ones first.
Where to next?
- Take a free AIF-C01 demo mock to gauge your current level.
- Browse AWS AI Practitioner study guides and cheat sheets
- Compare AWS AI Practitioner mock-test plans and pick the one that fits your timeline.