The vocabulary the exams and the daily work both assume you already have. Every entry gives the definition, the source it is drawn from, and the misreadings that follow from getting it wrong.
265 concepts, page 4 of 14
A system able to do more than its feature requires, the three forms it takes, and why permission design is the one control that still works after everything else has failed.
Artificial Intelligence
The three things that set how long a test runs, why the answer comes in whole weeks, the exposure point that decides who is counted, and the arithmetic showing that a one per cent ramp needs about twenty five times the duration of an even split.
Metrics
Scrum prescribes no forecasting technique. What it defines is that the Sprint Goal is the commitment, and that projections come from observation.
Scrum
Tokens, embeddings and transformers explained for somebody who has to make decisions about a model, with nothing in it about building one.
Artificial Intelligence
A funnel as a sequence of steps with a count at each one, the three choices that decide what it measures, a worked conversion calculation from forty thousand visitors down to four hundred and eighty customers, and why the denominator has to be stated before any rate means anything.
Metrics
The Gemini app, Gemini Advanced and Gems, Gemini for Google Workspace and Gemini Enterprise, and which one a described situation is asking for.
Artificial Intelligence
The failure where a system learns a goal that fits all of its training data and turns out to be the wrong goal once the distribution shifts.
Artificial Intelligence
Recognise vague language, avoided problems, goals presented as strategy and objectives that cannot guide work. Includes a practical document review.
Strategy
Charles Goodhart's 1975 observation, the three ways a product measure gets gamed once a team is judged on it, why most gaming is done by people acting in good faith, and the defences that keep a measure honest under pressure.
Metrics
The AI first argument, what enterprise ready means, the hardware underneath the platform and the open approach Google describes.
Artificial Intelligence
Gemini, Gemma, Imagen and Veo, what each is for, and how an organisation chooses between an open model it hosts and a managed one it calls.
Artificial Intelligence
Measures watched for damage rather than for improvement, the two kinds Kohavi, Tang and Xu separate, a table of common guardrails with what each one protects, and the arithmetic showing how a two per cent win can be a loss once latency is priced.
Metrics
The controls that sit around a model, why layering them never produces a boundary, and the one practitioners get wrong, which is what the application does with the response.
Artificial Intelligence
Everything an application quietly holds in the context window, why a system prompt was never a security boundary, and why leaked context is usually the step before the attack.
Artificial Intelligence
Facilitation carries the process while the group keeps the content, which is what separates helping an event reach its purpose from chairing it.
Scrum
The word conflict appears nowhere in the 2020 Scrum Guide, so the material comes from team research, and the one boundary the framework does draw is that the Scrum Master never decides for the team.
Scrum
Understand the AI agent loop, compare ReAct with plan and execute designs and set explicit limits on steps, tokens, time and tool actions.
Artificial Intelligence
The ways an AI feature goes wrong in production, ordered by how often a product team actually meets them, with the cause behind each and the control that has a chance against it.
Artificial Intelligence
Learn how LLMs turn prompts into tokens, use attention and generate responses, with practical implications for token budgets and reliable output.
Artificial Intelligence
Pre training, supervised fine tuning, reinforcement learning from human feedback and constitutional methods, and why a helpful model is a manufactured artefact that can be unmade the same way.
Artificial Intelligence