How the AIF-C01 exam works

On Tuesday, November 10, 2026, Noor Siddiqui's weekly AI hour at Cascade Ridge Veterinary Partners in Calgary looked different. For two months Noor, the data scientist the group hired for its AI assessment, had used the hour to teach Jonah Petrakis, the client services and scheduling analyst, how machine learning and generative AI work. Jonah had followed the whole series: the no-show model, the after-hours call summaries, the image-quality idea and the long discussion about where AI must stop and a veterinarian must decide. This time Noor pushed a single sheet of paper across the table. "You know this material," she said. "Now you need to know the exam." Jonah plans to sit the AWS Certified AI Practitioner exam, code AIF-C01, in April 2027. This course is the bridge between knowing the subject and passing the test, and it follows the twelve-part study guide Noor wrote for him that afternoon.

An exam readiness course does three jobs. It explains how the exam is built, so nothing on test day is a surprise. It reviews every content domain against the official exam guide, so you can find what you have forgotten. And it teaches strategy: how to read questions, how to manage time and how to plan the last weeks. You should already have completed AWS-111 to AWS-114, or have equivalent knowledge. If a review lesson here mentions an idea that feels new, go back to the course that teaches it properly before you carry on.

What the certification is for

AWS describes the AI Practitioner certification as a foundational credential. It is aimed at people who use or work around AI and machine learning on AWS without necessarily building models themselves: business analysts, project managers, product owners, sales and marketing staff, IT support and managers who must make sound decisions about AI. The target candidate is someone with up to about six months of exposure to AI and machine learning technologies on AWS, who uses those solutions but does not necessarily build them. That describes Jonah well. He will never train a large language model, but he will help decide whether the no-show pilot is worth keeping, what the after-hours summaries may and may not do, and how client data is protected along the way.

The certification does not test coding, mathematics beyond simple ratios, or the internals of neural networks. It tests whether you understand concepts precisely, whether you can match a business need to the right kind of AI and the right AWS service, and whether you recognize risks and the controls that address them. The exam guide also lists topics that are out of scope, such as developing or coding AI models, implementing data engineering or feature engineering techniques, tuning hyperparameters and building pipelines. Knowing what is out of scope saves study time.

The facts that matter on the day

Noor's sheet listed the facts AWS publishes, checked against the AWS certification page and the AIF-C01 exam guide on November 10, 2026. The exam lasts 90 minutes and has 65 questions. Fifty of them are scored and 15 are unscored. The unscored questions are ones AWS is testing for future use, and they are not marked, so you must treat every question as if it counts. The registration fee is 100 US dollars, and depending on where you live, taxes or currency conversion may change what you actually pay. You can sit the exam at a Pearson VUE testing centre or online with a remote proctor. The exam is offered in a dozen languages, including English, French, Japanese and several others; check the current list if you would prefer to test in French.

Results are reported as a scaled score from 100 to 1,000, and the minimum passing score is 700. A scaled score is not the number of questions you answered correctly. AWS converts your raw result to the scale so that different versions of the exam, which may differ slightly in difficulty, are treated fairly. The exam uses a compensatory scoring model, which means you need to pass the exam as a whole, not each domain separately. A weak domain can be made up by a strong one. Unanswered questions are scored as incorrect, and there is no penalty for guessing, so you should never leave a question blank.

The five content domains

The exam guide divides the scored content into five domains, each with a weight. Domain 1, Fundamentals of AI and ML, carries 20 of every 100 scored points. Domain 2, Fundamentals of generative AI, carries 24. Domain 3, Applications of foundation models, carries 28, the most of any domain. Domain 4, Guidelines for responsible AI, carries 14. Domain 5, Security, compliance and governance for AI solutions, also carries 14. With 50 scored questions, those weights work out to roughly 10, 12, 14, 7 and 7 questions, although the exact count on your form may vary a little.

Noor pointed out what the weights mean for study time. Domains 2 and 3 together carry 52 of every 100 points, so more than half of the exam is about generative AI and how applications are built on foundation models. That matches the courses: AWS-112 covered domain 2 and AWS-113 covered domain 3, while AWS-111 covered domain 1 and AWS-114 covered domains 4 and 5. In this course, lessons 3 to 8 review the domains in order. Domain 3 gets two lessons because it is the heaviest: one for design, prompting and retrieval, and one for customization and evaluation.

Task statements: the real syllabus

Each domain is broken into task statements, and each task statement lists the skills it expects. Domain 3, for example, has four task statements: design considerations for foundation model applications, prompt engineering techniques, the training and fine-tuning process, and evaluating foundation model performance. Under each, the guide lists specific items, such as the selection criteria for a model, the meaning of inference parameters like temperature, and evaluation metrics such as ROUGE, BLEU and BERTScore. Noor calls the task statements the real syllabus. "If a term appears in a task statement, assume a question can be built on it," she told Jonah. "If it does not appear anywhere, do not spend an evening on it."

The guide also names AWS services. Some appear as examples of a category, such as Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune and Amazon RDS for PostgreSQL as places to store embeddings. Others are central, such as Amazon Bedrock, Amazon SageMaker AI and Amazon Bedrock Guardrails. Newer names appear too, including Amazon Bedrock AgentCore, Strands Agents, Kiro and Amazon Quick. Services are renamed and launched often, so check the current guide in the weeks before your exam and note anything that has changed since you studied. The exam is written to the guide that is current when you sit it.

How AWS writes the questions

Every question has a stem, which is the scenario and the question itself, and a set of options. On multiple-choice questions, one option is correct and three are distractors, wrong answers designed to look reasonable to someone with partial knowledge. Distractors are rarely absurd. A typical distractor is a real AWS service that does something related but not what the scenario needs, or a real technique that would work but costs far more than the scenario allows. That is why lesson 9 of this course is devoted to commonly confused services: much of the difficulty of this exam is telling neighbours apart.

Many stems contain a constraint that decides the answer: the least operational overhead, the lowest cost, without managing infrastructure, without retraining the model, or with the most up-to-date information. Noor teaches Jonah to underline the constraint before reading the options. In lesson 2 you will practise this, along with the newer question types that AIF-C01 uses.

Delivery: testing centre or online

Jonah has to choose where to sit the exam. At a Pearson VUE testing centre, the centre provides the computer, the quiet room and the supervision, and you bring government-issued identification that matches the name on your registration exactly. Online proctored delivery lets you test from home or an office, but the room must meet the rules: a clear desk, no other people, no notes, a working webcam and microphone, and a stable internet connection. Before booking an online exam, you run a system test on the same computer and network you will use on the day. Problems with company laptops are common, because security software or blocked ports can stop the testing application, so a personal computer is often the safer choice.

Jonah lives about twenty minutes from a testing centre in Calgary and prefers to test away from home, where the dog and the doorbell cannot interrupt. Noor agreed. "Pick the setting where you will think most clearly," she said. "The exam is the same either way." He plans to book in January 2027 for a weekday morning in April, when he is usually most alert, and to leave a margin of a few weeks before the June storage-array deadline that will keep the infrastructure team busy.

Policies worth knowing before you book

AWS publishes its testing policies on its certification pages, and they apply to every exam, including AIF-C01. Three are worth knowing now. If you do not pass, you must wait 14 calendar days before you can sit the same exam again, and each attempt requires the full registration fee; there is no limit on the number of attempts. Final results are posted to your AWS Certification account within five business days, even though the exit screen at the end of the exam gives you information about your result. And once you pass an exam, you cannot sit the same exam again for two years. Rules can change, so read the current policy pages when you book, but none of these are likely to surprise you if you plan around them.

The certification is valid for three years. Noor reminded Jonah that renewal is part of the plan, not an afterthought. Lesson 12 covers the options for keeping the credential current and choosing a next step, such as an associate-level certification for people who build AI solutions.

weights = {"D1": 20, "D2": 24, "D3": 28, "D4": 14, "D5": 14}
hours = 40
scored = 50
for d, w in weights.items():
    print(d, "study hours:", round(hours * w / 100, 1), "| expected scored questions:", round(scored * w / 100, 1))

D1 study hours: 8.0 | expected scored questions: 10.0
D2 study hours: 9.6 | expected scored questions: 12.0
D3 study hours: 11.2 | expected scored questions: 14.0
D4 study hours: 5.6 | expected scored questions: 7.0
D5 study hours: 5.6 | expected scored questions: 7.0

The output above is a starting point, not a rule. Jonah's diagnostic, reviewed in lesson 11, shows that he needs more time on domain 5 than its weight alone suggests, and less on domain 1, which he knows well from building the no-show analysis with Noor.