Free, original, no-dumps exam prep
AWS Certified AI Practitioner (AIF-C01)
A foundational AWS certification for people who need to explain AI, machine learning, generative AI, foundation-model applications, responsible AI, and security or governance choices on AWS.
Independent resource: AI Certs Reviewer is not affiliated with or endorsed by AWS. Practice content is original and is not copied from live certification exams.
Answer-first overview
What to know before you study
Use these concise answers as an orientation, then verify registration details on the official provider pages before paying.
What does this credential cover?
A foundational AWS certification for people who need to explain AI, machine learning, generative AI, foundation-model applications, responsible AI, and security or governance choices on AWS.
Who is it for?
Business, product, sales, project, support, and early technical professionals with up to six months of exposure to AI or machine learning technologies on AWS.
How should I prepare?
Start with the official objective map, study one domain at a time, test the same domain with original practice, and route every missed question back to a lesson or syllabus topic.
Exam snapshot
Current public exam facts
- Level
- Foundational
- Duration
- 90 minutes
- Format
- 65 questions; 50 scored and 15 unscored
- Question types
- Multiple choice, multiple response, ordering, and matching
- Passing score
- 700 on a 100-1,000 scaled range
- Validity
- 3 years
Administrative facts can change. The official provider and testing-vendor pages remain authoritative for prices, availability, policies, languages, and scheduling.
Official-objective map
Domains to study
Weights are shown only when the provider publishes them. They guide study time; they do not predict the exact mix on an individual exam form.
Fundamentals of AI and ML
AI terminology, practical use cases, and the AI or ML development lifecycle.
Fundamentals of generative AI
GenAI concepts, capabilities, limitations, and AWS technologies for generative AI applications.
Applications of foundation models
Application design, prompting, training or fine-tuning decisions, and foundation-model evaluation.
Guidelines for responsible AI
Responsible development, fairness, transparency, explainability, safety, and human oversight.
Security, compliance, and governance
Securing AI systems and recognizing governance, privacy, and compliance requirements.
Practical study route
Turn the blueprint into practice
- Read the exam orientation and write the five domains and weights from memory.
- Build a service-selection map for common AWS AI, security, data, and monitoring scenarios.
- Practice original questions by domain and explain why every distractor fails the scenario.
- Run mixed, timed quizzes and route each miss back to the matching lesson or syllabus topic.