Artificial Intelligence Professional

Domains and learning objectives

A domain’s weighting is its share of the discipline, and the share of the examination paper drawn from that domain. The study material and the examination questions are written to the objectives.

1. Domain 1

   AI and Machine Learning Foundations

   20%

   • 1.1Distinguish artificial intelligence, machine learning, deep learning and rule-based automation by how each produces its outputs.
   • 1.2Classify a described problem as supervised, unsupervised or reinforcement learning and identify the target and features involved.
   • 1.3Select an appropriate model family (linear models, tree-based models, clustering, neural networks) for a given problem and justify the choice by data type, interpretability need and scale.
   • 1.4Interpret the roles of parameters, training, loss and optimisation in how a model learns, using simple numeric illustrations.
   • 1.5Explain how core deep learning architectures (feedforward networks, convolutional networks, sequence models and transformers) differ in the data they suit.
   • 1.6Identify the bias-variance trade-off, underfitting and overfitting from descriptions of model behaviour on training and unseen data.

2. Domain 2

   Data Foundations for AI

   15%

   • 2.1Assess the suitability of a dataset for a stated modelling task in terms of volume, representativeness, label quality and freshness.
   • 2.2Select appropriate treatments for missing values, outliers, duplicates and inconsistent categories in a described dataset.
   • 2.3Distinguish training, validation and test splits and identify splitting mistakes such as leakage and temporal contamination.
   • 2.4Apply basic feature concepts, including encoding categorical variables, scaling numeric variables and simple feature engineering, to spreadsheet-scale examples.
   • 2.5Evaluate the effect of class imbalance and sampling choices on what a model will learn.
   • 2.6Identify data governance obligations relevant to AI datasets, including consent, minimisation and retention at concept level.

3. Domain 3

   Model Development and Evaluation

   20%

   • 3.1Calculate accuracy, precision, recall, F1 and error rates from a small confusion matrix or result table.
   • 3.2Select the evaluation metric that matches a stated business cost of false positives versus false negatives.
   • 3.3Interpret regression evaluation measures (mean absolute error, root mean squared error, R-squared) and compare candidate models with them.
   • 3.4Distinguish the purposes of validation and test data and evaluate whether a described experiment supports its performance claim.
   • 3.5Evaluate baseline comparisons, hyperparameter tuning descriptions and simple experiment designs for soundness.
   • 3.6Interpret model explainability outputs, such as feature importance summaries, and identify their limits.

4. Domain 4

   Deployment, Operations and the AI Lifecycle

   15%

   • 4.1Evaluate whether a described business problem is a sound candidate for an AI solution and define measurable success criteria for it.
   • 4.2Select an appropriate deployment pattern (batch scoring, real-time API, embedded on-device, human-in-the-loop) for a stated use case.
   • 4.3Distinguish build, buy and API-based sourcing options for AI capability and assess their cost, control and data implications.
   • 4.4Interpret monitoring signals to identify data drift, concept drift and performance degradation in a deployed model.
   • 4.5Select appropriate responses to model degradation, including retraining, rollback and threshold adjustment.
   • 4.6Identify the documentation and versioning practices that make an AI system auditable and reproducible.

5. Domain 5

   AI Application Patterns and Generative AI

   15%

   • 5.1Distinguish predictive, generative and retrieval-based AI applications by their inputs, outputs and failure modes.
   • 5.2Explain how large language models produce text, including tokens, context windows and probabilistic generation, at concept level.
   • 5.3Select prompt design techniques (role setting, examples, structured output, decomposition) appropriate to a described task.
   • 5.4Evaluate when retrieval-augmented generation, fine-tuning or an unmodified foundation model is the right approach for a knowledge task.
   • 5.5Identify hallucination, prompt injection and over-reliance risks in described generative AI workflows and select mitigations.
   • 5.6Select appropriate human-in-the-loop checkpoints for AI-assisted work in fields such as drafting, translation, coding support and customer service.

6. Domain 6

   Responsible AI, Governance and Risk

   15%

   • 6.1Identify sources of unfair bias across the AI lifecycle, from historical data and labels to deployment context.
   • 6.2Interpret simple fairness evidence, such as approval or error rates broken down by group, and identify the concern it raises.
   • 6.3Distinguish transparency, explainability and accountability obligations for AI systems and match each to appropriate practices.
   • 6.4Evaluate privacy and security risks specific to AI, including training-data exposure, model inversion at concept level, and misuse of generated content.
   • 6.5Classify described AI systems into risk tiers of the kind used by the EU AI Act and identify the obligations that follow at concept level.
   • 6.6Select appropriate governance controls for an AI deployment, including impact assessment, human oversight, documentation and incident response.