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 1 of 14
The early action that separates people who stay from people who leave, how a candidate action is found by comparing retained and churned cohorts, the two properties a usable definition needs, and the correlation at the centre of every activation finding.
Metrics
Relative sizing, the delivery rate it produces, and how a range for a future date is forecast from what a team has actually done.
Project Management
Matching a solution type to a business need, the constraints that decide it, the steps to get it into an organisation, and how to measure whether it worked.
Artificial Intelligence
Attacks that target the model itself, rather than the software around it, and why several of them have no clean fix.
Artificial Intelligence
What changes when a model is given tools, memory and several steps to itself, and why the danger is in the permissions, whatever the intelligence.
Artificial Intelligence
How a commercial agreement is written when the scope is expected to change, and the mechanisms that let a buyer keep control of cost without fixing the scope in advance.
Project Management
Diagnose common AI agent failures and control retries, duplicate actions, invalid tool calls, plan drift and ambiguous tool results.
Artificial Intelligence
Design AI agent memory with clear ownership, source records, expiry and retrieval. Separate conversation summaries from authoritative application state.
Artificial Intelligence
The standards an AI system gets audited against, what data governance means once a model is involved, the review routine that keeps a deployed system accountable and the tooling that does each job.
Artificial Intelligence
What a language model genuinely does for a project manager today, what it does not, and the accountability, confidentiality and data questions that come with putting project material into one.
Project Management
What a team does on the day its system produces something it should not have, the decisions worth making months before that day, and when the law requires the incident to be reported.
Artificial Intelligence
Artificial intelligence, machine learning and deep learning are three nested categories, and the difference between them decides what a team can promise about a system.
Artificial Intelligence
The changes that cannot be randomised into two groups, the four designs available when a split is impossible, what each one assumes and how much smaller the claim becomes once the randomisation has gone.
Metrics
The discipline of obtaining the improvement for which the money was approved, which is a different thing from delivering the output, and which mostly happens after the project has closed.
Project Management
Run a bounded agent over fictional orders, validate tool requests and use SQLite transactions to prevent duplicate actions after a lost response.
Artificial Intelligence
Build a small retrieval pipeline, filter stale and private documents, measure recall and precision, and optionally generate an answer with checked citations.
Artificial Intelligence
Compare strategic importance, service requirements, total costs and exit risks before deciding whether to build, buy or partner.
Strategy
How a group of competent people becomes a team, through the stages of forming, storming, norming, performing and adjourning, and the psychological safety that lets it happen.
Project Management
What an organisation needs to run tests as routine work, how many tests can share the same traffic, the record that stops a question being asked twice, the arithmetic of experiment velocity and what a one in three success rate means for the way a programme is built.
Metrics
Gemini Enterprise Agent Platform, formerly Vertex AI, with Model Garden, the RAG offerings, custom agents and the rename the exam now expects.
Artificial Intelligence