About the exam
AIGP Exam structure
IAPP Artificial Intelligence Governance Professional prep with 601 original practice questions, domain-weighted mocks, flashcards, and topic recovery.
Issuer and path
IAPP AIGP Exam Prep is administered through IAPP. Check official resources before booking, retesting, or relying on a stale requirement.
Understanding the Foundations of AI Governance
18 scored + 0 pretest
AI governance foundations, responsible AI principles, stakeholder expectations, cross-functional roles, life-cycle policy, and third-party risk.
Understanding How Laws, Standards and Frameworks Apply to AI
21 scored + 0 pretest
Privacy law, intellectual property, nondiscrimination, consumer protection, product liability, AI-specific law, risk classification, and major standards.
Understanding How to Govern AI Development
23 scored + 0 pretest
Governance of AI design, building, data collection, training, testing, release readiness, monitoring, maintenance, incident management, and documentation.
Understanding How to Govern AI Deployment and Use
23 scored + 0 pretest
AI deployment decision factors, model selection, deployment options, vendor risk, impact assessment, ongoing monitoring, downstream harms, and external communication.
Before you schedule
Confirm you are purchasing the AIGP exam, review Pearson VUE options, ID requirements, membership or certification maintenance fee implications, retake rules, and whether the current Body of Knowledge version is the one you studied.
Official Outline Coverage Map
Coverage is mapped to official outline item counts so content depth can be checked without hard-coding a single exam.
| Topic | Official outline items | Your questions | Your flashcards | Confidence |
|---|---|---|---|---|
| AI Definitions, Risks and Responsible Principles | 5 | 35 | 8 | Priority |
| Governance Roles and Organizational Expectations | 6 | 42 | 8 | Strong |
| Lifecycle Policies, Procedures and Third-Party Risk | 7 | 50 | 8 | Priority |
| Privacy Laws Applied to AI | 5 | 34 | 8 | Priority |
| Existing Laws Beyond Privacy | 5 | 34 | 8 | Strong |
| AI-Specific Laws and Risk Classification | 7 | 48 | 8 | Priority |
| AI Standards, Frameworks and Tools | 4 | 32 | 8 | Good |
| Governance of AI Design and Building | 7 | 54 | 8 | Priority |
| Data Governance for Training and Testing | 7 | 54 | 8 | Strong |
| Release, Monitoring and Maintenance Governance | 9 | 55 | 8 | Priority |
| Deployment Decision Factors and Risks | 7 | 54 | 8 | Priority |
| Deployment Assessment and Vendor Risk | 6 | 46 | 8 | Strong |
| Governance of Deployment, Use and Ongoing Controls | 10 | 63 | 8 | Priority |
How to use this guide
How to study for IAPP AIGP
Treat each question as a governance decision: identify the AI role and life-cycle stage, classify the legal or risk issue, then choose the control, evidence, and accountability path.
1. Identify the life-cycle stage
Decide whether the scenario is intake, design, data, training, testing, release, deployment, monitoring, or incident response.
2. Classify role and risk
Name the stakeholders, provider or deployer role, affected parties, legal context, risk level, and harm path.
3. Select the control
Choose the policy, assessment, contract term, testing method, monitoring process, documentation, or communication control.
4. Preserve accountability evidence
Make sure ownership, review cadence, records, escalation, and remediation are visible.
AI Definitions, Risks and Responsible Principles
AIGP candidates must know accepted AI definitions, why AI requires governance, major AI risk types, AI-specific characteristics, and responsible AI principles.
Key rules
Rule 1
AI governance starts with a shared vocabulary for AI systems, model behavior, data dependency, autonomy, opacity, speed, scale, and probabilistic outputs.
Exam cue: Classify whether the issue is a definition, a risk characteristic, or a responsible AI principle.
Rule 2
AI harms can affect individuals, groups, organizations, and society through bias, misalignment, misuse, privacy loss, security failures, and unreliable decisions.
Exam cue: Use probabilistic output and opacity when explaining why ordinary software controls may not be enough.
Rule 3
Responsible AI principles commonly include fairness, safety, reliability, privacy, security, transparency, explainability, accountability, and human-centered design.
Exam cue: Connect harms to affected stakeholders, not just technical model performance.
Common traps
Treating AI governance as only a technical model-quality problem.
Prevention: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Assuming responsible AI principles are met just because a system is accurate overall.
Prevention: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Ignoring group or societal harms when the immediate user appears satisfied.
Prevention: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Memory anchors
AI Governance
AI governance is the system of roles, policies, controls, and oversight used to manage AI responsibly.
Probabilistic Output
Probabilistic output means AI results may vary and require risk-aware interpretation.
Opacity
Opacity is limited visibility into why an AI system produced a result.
Misalignment Risk
Misalignment risk occurs when an AI system's behavior does not match intended objectives or values.
Bias Risk
Bias risk is the chance that data, design, or deployment choices create unfair outcomes.
Responsible AI
Responsible AI applies principles such as fairness, safety, privacy, transparency, and accountability.
Human-Centricity
Human-centricity keeps affected people, rights, and meaningful oversight in the governance frame.
Accountability
Accountability assigns ownership for AI decisions, controls, monitoring, and remediation.
Next best moves
Quick check-up
Use a short quiz to confirm the rule pattern is actually sticking.
Check-up Questions
What best describes artificial intelligence as used in AI governance?
What is machine learning?
Answer all questions to submit.
Next step personalized recommendations
Open another topic next
Official resources
Verify the details with the official sources
Use these links for eligibility, scheduling, handbook rules, and issuer updates. Our guide helps you study; official sources tell you what the testing partner currently requires.
IAPP AIGP Certification Page
Official IAPP page describing the AIGP credential, why it matters, and exam preparation resources.
AIGP Body of Knowledge and Exam Blueprint
Official AIGP Body of Knowledge version 2.1 with domains, competencies, performance indicators, and blueprint ranges.
IAPP Certification FAQs
IAPP certification FAQ page covering scoring, exam results, certification maintenance, and related policies.
FAQ
Common AIGP questions
Is this the official IAPP AIGP exam?
No. These are original practice questions aligned to IAPP's public AIGP Body of Knowledge. They are not copied from secure exam material.
What does the current AIGP BoK cover?
Version 2.1 covers foundations of AI governance, laws and standards that apply to AI, governance of AI development, and governance of AI deployment and use.
How is the 601-question bank weighted?
The bank follows the BoK blueprint ranges, with heavier practice on laws, development governance, and deployment governance while still covering foundational AI governance.
What should I study first?
Start with AI governance vocabulary, risks, roles, and life-cycle policies, then move into privacy and AI-specific law, development controls, and deployment monitoring.
How should I use the 601 questions?
Use topic drills for a competency, section drills for one BoK domain, then weighted mocks once you can explain the control and accountability choice behind each answer.