Practice question for Risk and AI (RAI) Certificate 2026

MODULE 1: AI AND RISK: INTRODUCTION AND OVERVIEWEXAM WEIGHT: 8-12%

The application of artificial intelligence (AI) introduces a novel set of risks to all organizations that use it. This module offers a historical perspective on AI, an overview of both machine learning methodologies and generative AI, and an introduction to the risks associated with using AI/ML.

MODULE 2: TOOLS AND TECHNIQUESEXAM WEIGHT: 25-35%

This module, organized in 10 distinct chapters, provides an in-depth look at the following AI/ML tools and techniques:• Introduction to tools and techniques• Unsupervised learning• Supervised learning – Econometric techniques• Supervised learning – Machine learning techniques• Semi-supervised learning• Reinforcement learning• Supervised learning – Model estimation• Supervised learning – Model performance evaluation• Natural language processing• Generative AI and LLMs

MODULE 3: RISKS AND RISK FACTORSEXAM WEIGHT: 15-25%

This module provides a comprehensive overview of the primary risks associated with AI development and deployment. It discusses the numerous challenges associated with the creation of a “fair” algorithm, highlighting the different sources of bias that might affect algorithmic fairness. It also addresses the twin problems of explainability and interpretability, and other noteworthy risks, including risk to human autonomy, risk of AI-driven manipulation, reputational risk, existential risk, and global risks and challenges.

MODULE 4: RESPONSIBLE AND ETHICAL AIEXAM WEIGHT: 15-25%

This module builds on the risks examined in Module 3 and explores how ethical principles and governance can guide the development and deployment of AI technologies in a way that promotes trust, safety, and fairness. It also presents various ethical frameworks that can be applied to AI, the governance challenges associated with AI, and current global governance initiatives around AI.

MODULE 5: DATA AND AI MODEL GOVERNANCEEXAM WEIGHT: 15-25%

This module discusses data and model governance and provides a starting point to establish a firm-specific model validation framework across the entire AI/ML model life cycle — from model development through performance monitoring and decommissioning. The principles presented apply to a wide range of industries, but the primary focus is on the financial sector, and the quantitative risk models (QRMs) heavily relied upon and subject to formal regulatory oversight. The opacity of AI/ML models is also discussed, along with the need for proper governance of the data used to train these models.