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Green Belt · Promotion exam · AWA

AI Work Automator

Validates that you can automate your team's recurring work without code by combining projects, custom assistants, connectors and agent features, and run it while respecting permissions and data boundaries.

Daily practice · Free

10 questions · 20 min · No sign-up needed

Mock exam · Fair-exam rules

30 questions · 45 min · Free · In the app

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Promotion exam · Proctored

30 questions · 45 min · Raw score of 70 or more · $3.99 / one attempt

Promotion exam details

Exam overview

Questions
30
Time
45 min
Pass mark
70 pts
Recommended experience
White Belt (AI Literacy Foundation) level and at least 6 months of using AI tools as a team

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. 1. Work analysis and automation design20%

  2. 2. Knowledge spaces and custom assistants25%

  3. 3. Connectors and agent features25%

  4. 4. Sharing, permissions and data boundaries15%

  5. 5. Operations and improvement15%

Weights follow the Promotion exam. Practice and mock exams may mix domains differently.

What this exam verifies

  • Break work into units that can be automated and choose the right combination of features
  • Design projects (knowledge spaces) and custom assistants and roll them out to the team
  • Keep the permission scope of connectors and agent features to a minimum
  • Prevent information exposure with sharing, permission and data retention settings
  • Review automation output and improve it with operational metrics

Who it is for

  • Team leads who want to cut recurring work with AI
  • Administrators of internal AI tools
  • People responsible for no-code automation

Does not cover writing code yourself or API integration.

Domain details

Domain 1. Work analysis and automation design

  • Knowledge: Criteria of repetitiveness, risk and review cost, Human review points, Setting the scope of automation
  • Skills: Break a workflow into steps and decide where to apply AI

Domain 2. Knowledge spaces and custom assistants

  • Knowledge: Projects (knowledge spaces), Custom instructions, Project instructions vs. custom assistants, Knowledge capacity and retrieval
  • Skills: Write instructions for a team assistant, Design how materials are organized and how often they are updated

Domain 3. Connectors and agent features

  • Knowledge: Connectors and app connections, MCP-based connections, Agent mode, Scheduled tasks, Risks of browser agents
  • Skills: Connect with the minimum permissions needed, Add approval points to agent tasks

Domain 4. Sharing, permissions and data boundaries

  • Knowledge: Role-based sharing permissions, Workspace admin controls, Data retention and training-use settings
  • Skills: Design the scope of sharing, Keep sensitive materials separate

Domain 5. Operations and improvement

  • Knowledge: Review checklists, Usage and quality metrics, Change management
  • Skills: Improve instructions using failure cases, Measure adoption impact

Official documentation

OpenAI

Anthropic

Google

Green Belt Course · AI Work Automator

Practice, mock exams and every course book are free. Pay only when you take an official test.

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