Online exams. Verifiable credentials.

Associate Exam blueprint

1. 1AI and generative AI fundamentals 20%

   How AI, machine learning and generative AI work, explained in plain language. Candidates show they understand what large language models can and cannot do, and why their outputs must always be checked.

   • AI, machine learning, deep learning and generative AI: the key differences
   • How large language models are trained and how they generate text
   • Tokens, context windows and knowledge cut-off dates
   • Common limitations: hallucinations, outdated knowledge and inconsistent answers
   • Types of generative AI: text, images, audio, video and code
   • Key terms: model, prompt, training data, fine-tuning and multimodal
2. 2AI tools and assistants in the workplace 15%

   Choosing and using the right AI tool for everyday tasks. Covers general-purpose assistants, AI built into office suites and specialist tools, and the difference between approved enterprise tools and public consumer services.

   • General-purpose chat assistants and AI features built into office and collaboration suites
   • Specialist tools for research, meetings and transcription, images and presentations
   • Enterprise versus consumer tools: data handling, retention and approval
   • Selecting a tool by task, data sensitivity and the output you need
   • Working with files, images and web search inside an assistant
   • Recognizing when a task should not be done with AI
3. 3Prompt engineering 25%

   Writing clear instructions that produce reliable, useful results; the most heavily weighted domain. Candidates structure prompts, provide context and examples, and refine outputs through iteration.

   • Anatomy of an effective prompt: role, task, context, format and constraints
   • Zero-shot, few-shot (with examples) and step-by-step prompting
   • Specifying tone, audience, length and output format such as tables and lists
   • Iterating and refining with follow-up prompts, critique and rewriting
   • Breaking a complex task into a sequence of smaller prompts
   • Reusable prompt templates and common prompting mistakes to avoid
4. 4AI for productivity: content, summarization, analysis and reporting 15%

   Using AI to get everyday work done faster and better. Candidates apply AI to drafting, summarizing, analyzing information and preparing reports, while keeping accuracy and quality under human control.

   • Drafting and editing emails, letters, memos and other business content
   • Summarizing long documents, meeting notes and email threads
   • Extracting key points, decisions and action items from text
   • Basic data analysis: spotting trends, building tables and describing charts
   • Preparing reports and presentation outlines with AI assistance
   • Checking facts, figures and sources before using AI output
5. 5AI use cases and introduction to AI agents 10%

   Recognizing where AI adds value in a role or team, and understanding what AI agents are. Candidates identify suitable use cases and explain, at a foundational level, how agents differ from chat assistants.

   • Spotting repetitive, high-volume or knowledge-heavy tasks suited to AI
   • Estimating the time saved and benefit of a simple use case
   • Use cases by function: HR, finance, procurement, customer service and communications
   • What an AI agent is: goals, instructions, knowledge, tools and actions
   • Chat assistant, AI agent and automated workflow: how they differ
   • Why human oversight and approval points matter
6. 6Responsible AI, privacy and information security 15%

   Using AI safely, ethically and in line with organizational policy. Candidates show they can protect confidential and personal data, recognize bias and misuse, and remain accountable for AI-assisted work.

   • Data classification: what may and may not be shared with AI tools
   • Protecting personal and confidential information; core privacy principles
   • Verifying outputs: hallucinated facts and fabricated sources
   • Recognizing bias and fairness risks in AI outputs
   • Security risks: AI-generated phishing, deepfakes and prompt injection
   • Transparency, accountability and following your organization's AI policy