White Belt · Promotion exam · ALF
AI Literacy Foundation
Validates the shared fundamentals: understanding how generative AI produces answers and where it goes wrong, and using it safely at work. Whatever your role, everyone starts at White Belt.
Daily practice · Free
10 questions · 15 min · No sign-up needed
Promotion exam · Proctored
25 questions · 35 min · Raw score of 70 or more · $3.99 / one attempt
Promotion exam detailsExam overview
- Questions
- 25
- Time
- 35 min
- Pass mark
- 70 pts
- Recommended experience
- None
Every question has five choices and is marked on an OMR answer sheet. Multiple-choice and true-or-false questions ask for the right combination from a box; ordering and matching questions ask for the right arrangement. Judgment questions (L3) are worth 3 points and all others 2; the raw score is converted to a 100-point scale and graded 1 to 9.
Exam domains
1. How generative AI works and its limits25%
2. Effective instructions and iterative improvement25%
3. Choosing product forms and features20%
4. Responsible use and data15%
5. Adoption value and the organizational view15%
Weights follow the Promotion exam. Practice and mock exams may mix domains differently.
What this exam verifies
- Explain the limits of AI in terms of tokens, the context window, probabilistic generation and the knowledge cutoff
- Choose the product form that fits the task (chat, project, custom assistant, agent, API)
- Improve output quality with clear instructions, context, examples and output formats
- Follow the principles for handling sensitive information and verifying AI-generated output
- Compare the value and risks of adopting AI for a business use case
Who it is for
- Anyone who uses, or plans to use, generative AI at work, whatever their role
- Managers, PMs and executives evaluating AI adoption
- No coding experience is required
Does not cover writing code, system design or model training.
Domain details
Domain 1. How generative AI works and its limits
- Knowledge: Tokens, Context window, Probabilistic generation, Knowledge cutoff, Causes of hallucination, Reasoning models vs. standard models, Multimodal input
- Skills: Identify situations where model output must not be taken at face value, Choose a model tier that matches the difficulty of the task
Domain 2. Effective instructions and iterative improvement
- Knowledge: Specific instructions, Role and background, Providing examples, Specifying the output format, Breaking work into steps
- Skills: Rewrite vague requests as specific ones, Provide context by attaching materials, Review results and iterate
Domain 3. Choosing product forms and features
- Knowledge: Chat, Projects (knowledge spaces), Custom assistants, Connectors, Agent features, API
- Skills: Choose the right form for recurring tasks, questions over documents and integration with external apps
Domain 4. Responsible use and data
- Knowledge: Prohibited categories common to usage policies, Differences in data retention and training use between consumer and business plans, Admin controls
- Skills: Decide whether sensitive information may be entered, Check sources and verify facts
Domain 5. Adoption value and the organizational view
- Knowledge: Subscription vs. token-based pricing, Criteria for selecting use cases, Adoption impact metrics
- Skills: Classify business use cases by impact and risk, Propose measurable adoption metrics
Official documentation
White Belt Course · AI Literacy Foundation
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