AWS Certified AI Practitioner
Foundational AI, machine learning, and generative AI concepts on AWS — for business and technical roles alike, with no coding required.
Practice the real format
Do you think you are ready? Put your knowledge to the test with a free, timed practice exam that mirrors the AI Practitioner format — with instant scoring, per-domain breakdowns, and full answer explanations.
Start a practice exam →The AWS Certified AI Practitioner (AIF-C01) is a foundational certification that validates a broad understanding of artificial intelligence, machine learning, and generative AI concepts on AWS. It is designed for people who work with, or make decisions about, AI in a business context — including business analysts, project managers, and early-career technologists — as well as engineers who want a structured introduction to AWS AI services. It does not require you to build models. The best part is that you don't need any coding knowledge.
While studying for this course, you will learn how to efficiently use existing AI systems to solve problems and improve productivity. You will also gain an understanding of how AI models are developed, trained, and optimized for different use cases. In addition, the course explores the process of creating and training new AI models tailored for entirely new applications and business needs.
This certification also tests your understanding of the ethical and responsible use of AI. It covers the importance of using AI in a fair, transparent, and secure manner while minimizing risks and biases. Additionally, it introduces key techniques and best practices that help ensure AI is used responsibly and effectively in real-world applications.
Who Should Take This Certification?
Good fit for:
- Cloud consultants
- Business analysts
- Solution architects
- Data engineers
- Project managers working on AI initiatives
- Developers moving into GenAI
- Anyone involved in AI strategy discussions
However, this certification is a foundational-level certification designed to provide a broad understanding of AI and generative AI concepts, services, and use cases rather than deep technical expertise.
It helps learners build a strong baseline knowledge of AI fundamentals, responsible AI principles, and AWS AI/ML solutions.
Those seeking advanced hands-on skills, architecture design expertise, or in-depth development knowledge should consider pursuing the AWS Certified Machine Learning Engineer – Associate or AWS Certified Machine Learning – Specialty after completing this certification.
Read the exam structure guide to understand exactly what's tested, or the benefits guide to see why this certification is worth pursuing. When you're ready, take a full practice exam.
Your 4-week study plan
This plan structures your preparation around the four official exam domains, spreading roughly 6–8 hours of study across each week. Adjust the pace to your schedule — the goal is steady progress, ending with full-length practice papers to confirm you're ready.
- Complete a study session on AI vs. machine learning vs. deep learning and clearly map where generative AI sits within that hierarchy.
- Complete a review of core ML concepts: supervised, unsupervised, and reinforcement learning; training vs. inference; and common failure modes such as overfitting and bias.
- Complete a walkthrough of the AWS AI/ML stack — Amazon SageMaker plus the managed AI services (Rekognition, Comprehend, Transcribe, Translate, Textract, Polly, Lex) — and note when to use each.
- Complete a set of real-world scenario exercises that ask you to decide when AI is the right tool and when a simpler solution is preferable.
- Complete a deep dive into foundation models and large language models — understand tokens, embeddings, context windows, and the mechanics of text generation.
- Complete a practical session on prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and why prompt quality directly affects output quality.
- Complete a service review of Amazon Bedrock and Amazon Q — understand what each offers, which foundation models are available, and their appropriate use cases.
- Complete a concept study covering RAG (retrieval-augmented generation), fine-tuning, and model customization at a conceptual level — focus on when to apply each approach.
- Complete an extended study block on designing FM-based applications — this is the largest domain, so allocate at least two dedicated sessions this week.
- Complete a review of design trade-offs: model selection, inference parameters (temperature, top-k, top-p), latency, cost, and accuracy considerations.
- Complete an in-depth session on RAG architecture and knowledge bases — cover vector databases, embedding models, and how grounding reduces hallucination.
- Complete a study block on model evaluation methods: automated metrics, human evaluation, and aligning outputs to business goals.
- Complete a study block on responsible AI principles — fairness, bias detection, transparency, explainability, and how tools like Amazon SageMaker Clarify support these goals.
- Complete a review of AI security, compliance, and governance on AWS — cover IAM roles, data encryption, audit logging, and the shared responsibility model as it applies to AI workloads.
- Complete a targeted revision session on your weakest domains identified from the Week 3 practice paper — focus only on the topics your score says need work.
- Complete a final terminology and concept flashcard review across all five domains the day before your exam.
How to use this plan
Each week ends with a practice paper — treat that score as a checkpoint, not just a number. If any domain score is below 70%, spend an extra day on it before moving on. The Week 3 and Week 4 full-length papers are your best predictor of real exam performance. Once you're consistently above 80%, book with confidence.
Ready to test yourself?
Do you think you are ready? Put your knowledge to the test with a free, timed practice exam that mirrors the AI Practitioner format — with instant scoring, per-domain breakdowns, and full answer explanations.
Start a practice exam →