AWS AIF-C01 exam guide covering AI basics, Bedrock, responsible AI, and governance decisions.
This guide targets AWS Certified AI Practitioner (AIF-C01), AWS’s foundational certification for candidates who need to understand AI, ML, and generative AI on AWS without dropping into engineer-level implementation depth. The exam is mostly about business fit, service fit, risk fit, and control fit: what the problem is, what class of AI or GenAI pattern belongs there, which AWS-managed path fits, and which responsible-AI or governance control keeps the answer safe.
ML: Machine learning, which trains models from data so they can make predictions or classifications on new inputs.
Uses AI/ML on AWS, but does not necessarily build AI/ML systems
As of April 15, 2026, AWS’s current certification page and current exam guide are aligned on the domain structure, candidate level, duration, and question count. The exam guide adds the scored vs unscored split, question-type mix, and passing-score details that AWS uses on the live exam.
AWS frames AIF-C01 as a certification for people who are familiar with AI and ML on AWS, but who do not necessarily build the solutions themselves. That makes the best answer the one that keeps the right altitude: use case, pattern, service fit, risk, and governance boundary rather than low-level model-building detail.
AWS’s current exam guide breaks AIF-C01 into five weighted domains, and this online guide now follows that structure directly:
separate AI concept from AWS implementation service
distinguish prompting, RAG, fine-tuning, and traditional ML clearly
choose the managed AWS service that fits the use case instead of reaching for lower-level tooling
keep responsible AI, security, and governance in the same decision loop as capability
Where candidates usually lose points
Failure pattern
Better instinct
treating every AI question like a GenAI question
ask whether the problem is prediction, classification, search, generation, or governance first
picking the most advanced-sounding pattern
prefer the simplest AWS-native design that satisfies the stated business goal
answering like an ML engineer
stay in the practitioner lane unless the stem explicitly asks for deeper implementation detail
handling safety, privacy, and governance only after model choice
treat them as part of the answer, not as postscript controls
Review flow
flowchart LR
A["Study plan"] --> B["1. AI and ML fundamentals"]
B --> C["2. GenAI fundamentals"]
C --> D["3. Foundation model applications"]
D --> E["4. Responsible AI"]
E --> F["5. Security, compliance, and governance"]
F --> G["Cheat sheet, glossary, and final review"]
Best fit for this guide
If you are coming from…
Bias your review toward…
business, product, or project roles
use cases, service categories, and governance language
cloud or IT support
service fit, security boundaries, and AWS terminology
future AI or ML engineering path
foundational concepts now, then move into MLA-C01 next
If two answers both sound right
For AIF-C01, the better answer is usually the one that:
stays at the business and service-fit level unless the prompt explicitly asks about implementation detail
separates traditional ML, GenAI, RAG, fine-tuning, and prompt engineering cleanly
keeps responsible AI and security/governance as first-class decision lanes instead of treating them as afterthoughts