HIRAF study guide
HIRAF · Work through your exam, one topic at a time.
HIRAF is a professional certification exam focused on the governance and responsible deployment of artificial intelligence. It is aimed at practitioners who need to reason about AI systems beyond model accuracy: risk analysts, compliance and audit staff, data and ML engineers moving into governance roles, product managers overseeing AI features, and consultants advising organisations on AI controls. The HIRAF study guide has 6 sections and about 1,769 words.
01Fundamentals of Artificial Intelligence
Candidates must be fluent in the vocabulary that separates AI, machine learning, and deep learning: AI is the broad goal of machine intelligence, ML is the subset that learns patterns from data, and deep learning is ML using multi-layer neural networks. Know the classic problem types (classification, regression, clustering, ranking, generation) and how they map to supervised, unsupervised, self-supervised, and reinforcement learning.
329 words, about 2 min02Machine Learning and Model Development
Candidates should be able to narrate the full model development lifecycle: problem framing, data collection and labeling, feature engineering, model selection, training, evaluation, validation, deployment, and monitoring. Understand the bias-variance tradeoff and how it manifests as underfitting versus overfitting.
300 words, about 2 min03Data Management and Governance
This domain covers how data is sourced, described, protected, and kept fit for AI use. Understand data quality dimensions - accuracy, completeness, consistency, timeliness, uniqueness, and validity - and how each affects model behavior.
284 words, about 1 min04AI Risk Management and Ethics
Candidates should understand risk management as a continuous cycle: identify, assess, mitigate, monitor, and communicate. Know the harm categories AI systems can create - bias and unfairness, privacy harms, safety incidents, opacity, misuse, and environmental or economic impacts.
287 words, about 1 min05AI Governance, Regulation and Compliance
Candidates must understand governance as the structures, policies, and accountability mechanisms that direct AI use. Know the distinction between binding regulation, standards, and voluntary frameworks, and how they interact.
292 words, about 1 min06AI Systems Implementation and Operations
Candidates should understand what changes when a model moves into production: integration with applications and data pipelines, latency and throughput constraints, scalability, and cost management. Know the components of an operational AI stack - data ingestion, feature pipelines, model serving, monitoring, and logging.
277 words, about 1 min