HomeCoursesAIGP - AI Governance Professional - Exam-Prep

☁️ AIGP - AI Governance Professional - Exam-Prep

Free exam-prep for AIGP - AI Governance Professional (IAPP AIGP) with a signed certificate. Learn the modules, pass the 10-question exam, EN/FR/AR, no account.

Last updated: June 2026

For privacy, legal, risk, compliance and AI/ML professionals: build the end-to-end AI governance knowledge to confidently pass the independent IAPP AIGP exam. The course is organized into 11 modules, ending with a final exam (pass mark 70%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

  • Foundations of AI: Core Concepts, Machine Learning and the AI System Lifecycle
  • Responsible AI Principles, Risks, Harms and Ethical Considerations
  • Building and Operationalizing an AI Governance Program
  • The EU AI Act: Risk-Based Tiers, Obligations and Enforcement
  • Global AI Laws & GDPR Intersections: US State Laws and Emerging Regulation
  • Frameworks and Standards: NIST AI RMF, OECD Principles and ISO/IEC 42001
  • Governing AI Development: Design, Data Governance and Model Training
  • Testing, Validation, Bias Mitigation and Development Documentation
  • Governing AI Deployment: Readiness, Human Oversight and Vendor/Third-Party Risk
  • Post-Deployment Monitoring, Incident Response and Responsible Decommissioning
  • Exam Strategy and Timed AIGP Mock Exam

Learning objectives

  • Explain core AI, machine learning and generative AI concepts and the AI system lifecycle.
  • Articulate responsible-AI principles (fairness, transparency, accountability, safety, human oversight) and the sources of AI risk and harm.
  • Map the global AI legal landscape, including the EU AI Act's risk-based tiers, GDPR intersections, and US and other emerging AI laws.
  • Apply key frameworks and standards: NIST AI RMF (Govern, Map, Measure, Manage), OECD AI Principles and ISO/IEC 42001.
  • Design and operationalize an enterprise AI governance program, including roles, policies, risk management and impact assessments.
  • Govern the AI development lifecycle: data governance, model training, testing, validation, documentation and bias mitigation.
  • Govern AI deployment and use: vendor/third-party risk, human oversight, post-deployment monitoring, incident response and decommissioning.
  • Practice with domain-weighted questions and a timed mock exam aligned to the AIGP blueprint.