AWS Certified AI Practitioner (AIF-C01) Study Guide & Exam Tips

The AWS Certified AI Practitioner (AIF-C01) is a foundational credential for anyone working with AI and machine learning on AWS. You answer 65 questions in 90 minutes and pass at a scaled score of 700 out of 1000. No data science background required. Roughly six months of exposure to AI/ML concepts and AWS services puts you in range.

Short on time? Practice with realistic AIF-C01 questions, each answer includes full explanations across all five exam domains.

What is the AWS AI Practitioner certification?

The AIF-C01 sits at the foundational level, alongside Cloud Practitioner. It targets business analysts, developers, product managers, IT professionals, and anyone whose role touches AI but isn't focused on building models from scratch. The exam tests understanding and judgment, not coding or math.

If you've used AWS or played with ChatGPT or Bedrock, you're ahead. If both are new, it's still doable; you'll just need more time for vocabulary.

Exam format and logistics

You get 65 questions in 90 minutes, roughly 1.5 minutes per question on average. Only 50 count toward your score; the other 15 are unscored trial questions AWS uses to test future content. You can't tell them apart, so treat every question as live.

Scoring is scaled and compensatory. Scaled means your raw percentage gets converted to a 100–1000 score that accounts for question difficulty. Compensatory means you don't have to pass each domain separately; a weak area can be balanced by a strong one.

The exam uses newer question formats. Beyond standard multiple-choice and multiple-response, you may see ordering questions (arrange steps correctly) and matching questions (pair items from two lists). They test the same knowledge; they just look different.

Exam domains and weights

The exam is built around five domains. Focus your study time on the heavier ones.

Fundamentals of AI and ML (20%): AI vs. ML vs. deep learning, types of learning (supervised, unsupervised, reinforcement), and where ML fits in real workflows.

Fundamentals of GenAI (24%): Foundation models, large language models, tokens, embeddings, prompt engineering basics, and Amazon Bedrock.

Applications of Foundation Models (28%): The largest domain. Picking the right model, retrieval augmented generation (RAG), fine-tuning vs. prompt engineering, evaluating model output, and designing real applications.

Guidelines for Responsible AI (14%): Bias, fairness, transparency, explainability, and the tradeoffs of deploying AI responsibly.

Security, Compliance, and Governance for AI Solutions (14%): Protecting data used with models, governance, and compliance for AI on AWS.

Generative AI and foundation models together make up 52% of the exam. That's the heart of AIF-C01.

How hard is the AIF-C01?

It's a foundational exam, so it doesn't require deep math or code. There's no Python, hyperparameter tuning, or confusion matrices.

What it does test is whether you can apply concepts. A typical question describes a business situation and asks which approach or AWS service fits best. For example: when does RAG make sense over fine-tuning? When does a managed service beat training your own model? The facts are straightforward. Matching them to the scenario is the skill.

If you've used AWS or touched generative AI, this is approachable. If both are new, you'll need more ramp-up time on vocabulary.

The exam rewards judgment over memorization. No trick questions. Just realistic scenarios where you pick between reasonable options.

Study plan: 2–4 weeks

Most people with cloud or tech background need two to four weeks at an hour a day. If you're already comfortable with AWS and have played with Bedrock or LLMs, a week or two works. If everything here is new, give yourself four to six weeks.

Week 1: Fundamentals. AI vs. ML vs. deep learning, types of learning, and where each fits. Get the vocabulary solid so harder domains make sense.

Week 2: Generative AI and foundation models. This is 52% of the exam. Learn what foundation models are, how prompting works, RAG vs. fine-tuning, and Bedrock's role.

Week 3: Responsible AI, security, and governance. Bias, fairness, explainability, and how AWS handles data protection and compliance for AI.

Week 4: Practice questions only. Timed full-length exams under exam conditions. Review every answer, right ones included, until you know exactly why the right option wins.

Don't try to memorize every ML algorithm. The exam rewards understanding what's available and making good choices.

How to prepare and pass

Reading about foundation models teaches you what they are. Practice questions teach you how AWS expects you to choose between options, which is what the exam scores. Start answering questions earlier than feels comfortable, even before you feel ready.

For every question, work out why the correct answer beats the others. Do this even for ones you get right, because guessing correctly and understanding are not the same.

Habits that move your score:

• Get hands-on if you can. Open Amazon Bedrock, try a few prompts, and the generative AI domain stops being abstract.
• Practice the newer question types. Ordering and matching feel unfamiliar. Try them before exam day so the format doesn't slow you down.
• Keep a cheat sheet of pairs you confuse. Fine-tuning vs. RAG. Supervised vs. unsupervised. Drill these pairs until they're automatic.
• Simulate the exam. Take full-length, timed practice tests. This reveals weak spots and builds 90-minute stamina.

Practice with exam-style questions across all five domains. Each answer includes full reasoning for why every option is right or wrong.

Is the AI Practitioner exam worth it?

Yes, if your work touches AI even loosely. It's a low-cost, foundational way to prove you understand generative AI and how it runs on AWS. It pairs well with Cloud Practitioner if you want to round out AWS basics. It's also a smart first step before more advanced certs like Solutions Architect.

Interested in AWS certifications more broadly? Map the full AWS certification path, or see how Cloud Practitioner vs. Solutions Architect compare.

Frequently asked questions