The foundational AI credential from Amazon Web Services, aimed at people who use AI systems in their work. AWS assesses it in one 90 minute exam covering machine learning fundamentals, generative AI, foundation models, responsible AI and the governance around all of it.
Practice questions (65)Free. Sign in to answer, so your result is kept and reported by module.
At a glance
Cost
$100 AWS lists the exam at $100 USD. Prices in other currencies follow the published exchange rates, and there is no separate registration fee. AWS charges the full price for a retake and enforces a 14 day wait between attempts.
Format
65 questions in 90 minutes, of which 50 are scored and 15 are unscored trial questions that AWS does not mark as such. Question types are multiple choice, multiple response, ordering, matching and case study. Candidates sit it at a Pearson VUE test centre or online with a proctor.
Pass mark
700 on a scaled range of 100 to 1,000. AWS does not publish the number of questions this corresponds to, because the scale equates forms of differing difficulty. The score report gives section level feedback, and the overall scaled score decides the result.
Prerequisites
None. AWS suggests up to six months of exposure to AI and machine learning on its platform, and recommends AWS Cloud Practitioner Essentials first for anyone new to the cloud. Neither is enforced at booking.
Renewal
Valid for three years. A candidate recertifies by passing the current version of this exam again, or by earning AWS Certified Machine Learning Engineer Associate, which supersedes it.
Based on
The AIF-C01 exam guide, version 1.4, which lists five content domains with published weightings and a task statement breakdown under each
What it covers
What it covers. Amazon Web Services sets and awards this certification, and Product Digest supplies the preparation for it. The exam tests judgement, since AWS states plainly that developing or coding models is out of scope, and the questions ask which technique suits a situation, what a result means and what could go wrong. Product people spend their working time on exactly those decisions, so the exam suits them.
The weight sits on generative AI. Foundation models and their applications carry 52 per cent between them, covering model selection, inference parameters, prompt engineering, Retrieval Augmented Generation, fine tuning and evaluation. Classical machine learning is a fifth of the exam and sits under the rest as grounding.
Responsible AI, security and governance carry 28 per cent between them. Candidates from a product background find this the easiest reading and skip it most often in preparation, which leaves more than a quarter of the exam unrevised.
AWS service names matter. The exam is vendor specific, and a question will describe a situation and expect the candidate to know that Amazon Bedrock hosts foundation models, SageMaker Clarify looks for bias and Guardrails filters output. Learning the concept without the AWS service that implements it will not be enough.
The syllabus5 domains · free
Amazon Web Services’s own domains. Each opens its own page listing the concepts beneath it, and each concept has a page of its own.
A logistics company has a system that decides whether a shipment needs a customs declaration. The logic was written by a compliance team as a set of conditions taken from published trade rules, and it is updated whenever the rules change. A stakeholder asks whether the company is already using machine learning. What is the accurate answer?
AYes. Any system that automates a decision a person used to make is machine learning by definition.
BYes, because the conditions were derived from real trade data held by the compliance team.
CNo. The behaviour was specified by people rather than learned from data, so it is artificial intelligence without machine learning.
DNo, and it is not artificial intelligence either, because no model is involved.