Amazon Web Services · 2026 Edition
A complete preparation guide written by Amazon Web Services-certified engineers. Covers the exam format,all 5 blueprint domains, a week-by-week study plan, and proven tips for passing first time.
4–8 weeks
Prep time
Beginner–Intermediate
Difficulty
50
Exam questions
700/1000
Pass mark
Exam code
AIF-C01
Full name
AWS Certified AI Practitioner
Vendor
Amazon Web Services
Duration
90 minutes
Questions
50 items
Passing score
700/1000 (scaled)
Domains covered
5 blueprint domains
Recommended experience
No ML background required; basic familiarity with AWS console helpful
Typical prep time
4–8 weeks
The AWS Certified AI Practitioner (AIF-C01) validates foundational knowledge of AI, ML, and generative AI on AWS — covering Amazon Bedrock, SageMaker basics, and responsible AI principles. It is the entry point for professionals who work with AI-powered AWS services without needing to build models from scratch.
Job roles this opens
Domain percentage weights are not currently available for this exam. The checklist below is still useful for planning your study.
Week 1–2
AI/ML Fundamentals: supervised vs unsupervised vs reinforcement learning, key ML concepts, training vs inference
Tip: The exam tests conceptual understanding, not maths. Focus on when to use each ML type, what overfitting/underfitting mean, and the difference between classification, regression, and clustering problems.
Week 3–4
Generative AI & Foundation Models: LLMs, prompt engineering, RAG, Amazon Bedrock, model selection
Tip: Bedrock is central to this exam. Know its key capabilities: access to foundation models (Anthropic Claude, Meta Llama, Amazon Titan), Knowledge Bases for RAG, Guardrails for safety, and Agents for multi-step tasks. Understand when to fine-tune vs use RAG vs use prompt engineering.
Week 5–6
AWS AI Services: Rekognition, Comprehend, Transcribe, Polly, Translate, Textract, Forecast, Personalize, Lex
Tip: Know each service's primary use case in one sentence. Exam scenarios describe a business problem — you match it to the right managed AI service. Rekognition = images/video; Comprehend = NLP/sentiment; Transcribe = speech-to-text; Textract = document extraction.
Week 7–8
Responsible AI, Security & MLOps: bias, fairness, explainability, SageMaker basics, model monitoring
Tip: Responsible AI has grown in exam weight. Know Amazon Bedrock Guardrails (content filtering, PII redaction, topic denial), the six pillars of responsible AI (fairness, explainability, privacy, safety, veracity, robustness), and how to detect and mitigate bias in ML models.
AIF-C01 is scenario-based — you are given a business situation and must select the right AWS AI service or Bedrock feature. Practice mapping problems to services, not memorising API names.
Prompt engineering is tested: know zero-shot, few-shot, and chain-of-thought prompting. Know what a system prompt does, and why temperature and top-P settings affect output variability.
RAG (Retrieval-Augmented Generation) is a major topic: know that it grounds LLM responses in your own data, reduces hallucinations, and is implemented on AWS via Bedrock Knowledge Bases with a vector store (OpenSearch or Aurora).
Understand the ML lifecycle: data collection → data preparation → model training → evaluation → deployment → monitoring. Know what SageMaker covers at each stage, even at a high level.
Responsible AI terms tested: hallucination (model generates false information), grounding (anchoring responses in retrieved facts), bias (systematic error from skewed training data), explainability (ability to interpret why a model made a decision).
Apply everything in this guide with adaptive practice questions, detailed answer explanations, and domain analytics.