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Certification

Certification in Implementing AI Governance, Risk, and Compliance Programs

Get certified in AI Governance (IAPP AIGP). Apply the OECD 7-stage lifecycle, align to cross-region laws, set roles, build required artifacts, draft policy, run risk reviews, and deliver audit-ready AI,ready to lead programs on day one.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

What the exam covers

2 questions from each of the 7 chapters of the course

14 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Introduction and agenda3:08
  2. 02Instructor background5:23
  3. 03AIGP certification overview6:09
  4. 04Why get AIGP certified7:52
  5. 05Version 2.1 updates11:12
  6. 06How to get started2:24
  7. 07Q&A session24:05
About the course

IAPP AIGP Certification Prep: AI Governance Online Course (Video Course) gives you a clear, no-fluff path to the latest AIGP v2.1,OECD's seven-stage lifecycle, cross-region laws, and exam-style scenarios. You'll build practical artifacts, master the right roles, and sharpen judgment for improved decision-making, a real competitive advantage, higher productivity, and adaptability,and a future-proof career. Enroll now to walk into the exam,and your next governance meeting,ready to lead with evidence.

This certification covers the following topics:

  • AIGP v2.1 changes and their impact on scope, domains, and exam emphasis
  • Exam structure and scenario tactics for v2.1
  • OECD seven-stage AI lifecycle: plan, data collection/prep, build/validate, test, deploy, operate/monitor, decommission
  • Cross-jurisdiction AI laws and guidance: US federal/state, EU (EDPB, anonymization), China
  • Standards alignment: NIST AI RMF, ISO/IEC 42001, ISO/IEC 42005
  • AI impact assessments (ISO 42005): making risk visible and actionable with evidence
  • Roles and responsibilities across the lifecycle; operator terminology in context
  • Governance artifacts and proofs: registers, model cards, data lineage, evaluation reports
  • Explainability-by-design and ethics-by-design applied in real workflows
  • Building a practical AI governance operating model and lightweight, high-trust workflows
  • GPAI and foundation models: obligations, enforcement themes, and global scope
  • Common pitfalls and how to avoid them