Full exam simulation
65 questions · 90 minutes, 15 unscored and not identified. One question per screen, flag and strike-out, a review screen and a score report per domain — no answers until you finish.
Amazon Web ServicesAIF-C01FoundationalAI & MLAvailable
AWS Certified AI Practitioner
AI, ML and generative AI concepts on AWS: foundation models, Amazon Bedrock, responsible AI and governance.
Independent practice — not affiliated with or endorsed by Amazon Web Services. AIF-C01 is used only to identify the exam. Trademark notices
Domain weights
Share of scored content per domain. Every sitting draws questions by these weights.
- D1Fundamentals of AI and ML20 %20 concepts
- D2Fundamentals of GenAI24 %24 concepts
- D3Applications of Foundation Models28 %28 concepts
- D4Guidelines for Responsible AI14 %14 concepts
- D5Security, Compliance, and Governance for AI Solutions14 %14 concepts
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ESL +30
The full sitting with 30 extra minutes (120 min) — the accommodation non-native English speakers can request for exams in English.
Quick 20
20 questions · 28 min, same weights, all scored. A daily check.
Weak spots
20 questions aimed at your weakest domains, missed concepts and due reviews.
Practice builder
Pick domains, topics, how many questions and which pool (new, wrong, due, low confidence). Spaced repetition with confidence ratings, flashcards and a mistake book.
Open practiceDomain drill
One domain, up to 20 questions.
What's on the exam
Domain 1: Fundamentals of AI and ML
20 %Basic AI and ML concepts and terms, practical AI use cases (and when not to use AI), and the ML development lifecycle.
Practice domain 1 →Domain 2: Fundamentals of GenAI
24 %Generative AI concepts (tokens, embeddings, foundation models), its capabilities and limits for business problems, and AWS services for building GenAI apps.
Practice domain 2 →Domain 3: Applications of Foundation Models
28 %Design considerations for foundation-model applications, prompt engineering, training and fine-tuning, and evaluating model performance.
Practice domain 3 →Domain 4: Guidelines for Responsible AI
14 %Developing responsible AI systems (bias, fairness, inclusivity, safety) and the importance of transparent, explainable models.
Practice domain 4 →Domain 5: Security, Compliance, and Governance for AI Solutions
14 %Securing AI systems, and the governance and compliance rules and AWS tools that apply to AI solutions.
Practice domain 5 →
Domain names and weights come from the official AIF-C01 exam guide (checked 2026-10-26). Always check it for the authoritative scope.
The real exam also has ordering and matching questions; the simulator uses multiple-choice only.
Sample questions
Two of this exam's original questions, with the explanation you get after every answer.
Sample 1Select one
Which statement correctly describes the relationship between artificial intelligence (AI), machine learning (ML), and deep learning?
- AML and deep learning are separate fields that do not overlap with AI.
- BDeep learning is a subset of AI but is not a form of ML.
- CDeep learning is a subset of ML, and ML is a subset of AI.
- DAI is a subset of ML, and ML is a subset of deep learning.
Show answer & explanation
Correct: C. AI is the broad field of building systems that perform tasks that normally require human intelligence. ML is a subset of AI in which systems learn patterns from data instead of following explicitly programmed rules. Deep learning is a subset of ML that uses multi-layered neural networks.
Sample 2Select 2
Which statements about generative AI and agentic AI are correct? (Select TWO.)
- AGenerative AI models are typically large models pre-trained on very large and diverse datasets.
- BAgentic AI systems cannot interact with external systems or APIs.
- CAgentic AI commonly uses a foundation model to reason, plan, and decide which tools to call.
- DGenerative AI models can only be built with rule-based logic.
- EGenerative AI and traditional ML models cannot be used together in one application.
Show answer & explanation
Correct: A, C. Generative AI is powered by foundation models that are pre-trained on broad data. Agentic AI builds on these models: the model reasons about a goal, plans steps, and invokes tools or APIs to take actions. Generative AI and traditional ML are frequently combined in one solution.