AIF-C01 Exam Guide: Format, Cost, Domains, and How to Prepare

The AWS Certified AI Practitioner (AIF-C01) is AWS's foundational AI certification — validating fluency in AI/ML concepts, generative AI, Amazon Bedrock, and responsible AI practices. It's designed for people who use or evaluate AI solutions, not necessarily build them.

This guide summarizes official exam details — confirm the current version on the AWS certification page before booking.

Exam format at a glance

• 65 questions — multiple choice, multiple response, plus ordering and matching formats unique to this exam
• 90 minutes — about 1.4 minutes per question
• $100 USD
• Passing score: 700 on a scaled range of 100–1000
• 50 scored + 15 unscored questions (unscored items aren't identified)
• Pearson VUE testing center or online proctored exam

The five domains

• Domain 1: Fundamentals of AI and ML — 20%
• Domain 2: Fundamentals of Generative AI — 24%
• Domain 3: Applications of Foundation Models — 28% (the largest — Bedrock, RAG, prompt engineering, agents)
• Domain 4: Guidelines for Responsible AI — 14%
• Domain 5: Security, Compliance, and Governance for AI — 14%

What the exam actually tests

AIF-C01 is conceptual, not code-heavy. Expect 'which technique/service fits this scenario' questions: RAG vs fine-tuning vs prompt engineering for a knowledge task; embeddings and vector stores for semantic search; Textract vs Comprehend vs Rekognition for document/text/image tasks; Bedrock Guardrails for content safety.

Watch the ordering/matching formats — e.g., rank inference options by latency or match responsible-AI dimensions to scenarios. They reward understanding relationships, not just memorization.

A realistic prep plan

Most candidates prepare in 2–6 weeks. Priorities by weight: spend the most time on Domain 3 (foundation model applications — RAG, prompt techniques, Bedrock features) and Domain 2 (generative concepts — tokens, embeddings, temperature).

• Week 1: AI/ML basics — supervised vs unsupervised vs generative, model lifecycle
• Week 2: GenAI fundamentals — FMs, tokens, embeddings, inference parameters
• Week 3: Bedrock + FM applications — RAG, agents, prompt engineering, managed AI services
• Week 4: Responsible AI + security/governance — bias, transparency, PII, guardrails
• Final days: practice sets covering every domain, review all explanations