Track OAI-FOUND 7 domains 2 × 60-item mocks Academy-aligned

This is the entry track. It assumes no prior AI experience and builds, from first principles, the working knowledge a knowledge worker needs to use ChatGPT well and judge its output. If you are new to AI, start here; every other OpenAI track on this site assumes what this one teaches.

What this track prepares you for

This track mirrors the OpenAI Academy course AI Foundations – a 60–75 minute, ChatGPT-based course and the first course in the Foundations pathway (AI Foundations → Applied AI Foundations → Agents and Workflows). The Academy course covers AI basics, prompting, context and responsible use; this track goes deeper on each so that recall and judgment survive a randomised assessment.

To be precise about what a pass earns you: completing the Academy course and scoring at least 80% on its assessment earns an OpenAI Academy badge issued through Accredible. That badge, and any pathway certificate of completion, is not a certification and does not guarantee eligibility for a future OpenAI certification – that is OpenAI’s own wording. The mock exams here are independent practice, built from publicly available learning objectives; they are not official OpenAI questions and there is no public OpenAI exam to have questions from. See the credential landscape and assessment model pages for the full picture.

Blueprint

#	Domain	Weight	Items (of 60)	Track page
1	AI and Generative AI Fundamentals	14%	~8	Domain 1
2	How Language Models Behave	16%	~10	Domain 2
3	ChatGPT Surfaces and Features	14%	~8	Domain 3
4	Prompting and Instructions	20%	~12	Domain 4
5	Context, Files and Memory	12%	~7	Domain 5
6	Verifying and Evaluating Output	14%	~8	Domain 6
7	Responsible and Safe Use	10%	~6	Domain 7

Where the marks are

Prompting (20%), How Language Models Behave (16%) and the two evaluation-flavoured domains – ChatGPT Surfaces (14%) and Verifying Output (14%) – together are 64% of the mock. This track rewards knowing why the model behaves as it does and how to steer and check it, far more than it rewards memorising product names.

Two independent mock exams

This track ships two full-length, domain-weighted independent mock exams of 60 items each. Mock Exam 1 is your diagnostic – sit it first, untimed, to find your two weakest domains. Mock Exam 2 is deliberately harder (more multi-constraint stems and FIRST / BEST / MOST cost-effective / TWO qualifiers) and timed – use it as your readiness gate before you enrol in and sit the real Academy assessment. All items across both mocks and the domain pages are distinct.

The mindset this track rewards

The Academy course frames ChatGPT as tutor, practice environment and feedback loop at once. The assessment rewards one posture throughout: ChatGPT is a fast, fluent, fallible collaborator whose output you own. Correct answers consistently:

Treat fluent, confident text as a draft to verify, not a fact to forward.
Give the model the context it needs instead of expecting it to guess.
Match the effort and the surface to the stakes – a quick reply for a low-stakes task, reasoning and verification for a consequential one.
Keep sensitive data out of tools and conversations that are not sanctioned for it.
Route irreversible, regulated or external work through a human review gate.

Wrong answers trust the output because it reads well, paste confidential data without checking policy, expect one giant prompt to do everything, or treat the model as a search engine that cannot be wrong.

Suggested time allocation

A 12–15 hour plan, weighted by weight × how new the material is to you.

Domain	Weight	Hours
Prompting and Instructions	20%	3
How Language Models Behave	16%	2.5
AI and Generative AI Fundamentals	14%	2
ChatGPT Surfaces and Features	14%	2
Verifying and Evaluating Output	14%	2
Context, Files and Memory	12%	1.5
Responsible and Safe Use	10%	1.5

Hands-on preparation checklist

Do these in a real ChatGPT account. Reading about prompting teaches you nothing about prompting.

Ask ChatGPT the same factual question three times in new chats and note where the wording of the answer differs – this is non-determinism in the raw.
Take one weak prompt and rebuild it with role, task, context, constraints, format and a success criterion; compare the two outputs side by side.
Ask for an obscure statistic with no source, then ask ChatGPT to cite it – observe how a hallucinated citation looks.
Switch between a fast model and a reasoning model on a multi-step problem and compare quality, latency and the visible reasoning.
Create a Project with custom instructions and one uploaded file, then ask a question that only the file can answer.
Turn a source document into a Search query and a deep research request and compare what each returns.
Add a fact to memory, start a new chat, and confirm the model uses it; then find and delete that memory.
Draft an external customer email, then write the human-review checklist you would apply before sending it.