How to study for the AIGP

Start from the current BoK

Download the Body of Knowledge v2.1 from iapp.org and treat it as your map. It is the exact blueprint the exam is built on, and it tells you every concept that is testable. Confirm that every other resource you use references v2.1, because much of the material online still targets the old seven-domain structure or the previous version. Studying an outdated BoK is the most wasteful mistake a candidate can make.

Allocate time by domain weight

The four domains do not carry equal weight, and the foundational domain is the smallest. The most common preparation error is spending the most time on Domain 1 because it feels like the foundation, when the exam allocates most of its marks elsewhere. Read the weightings in the current BoK and divide your study time in proportion. Time spent over-studying a light domain is time not spent on a heavy one.

Build understanding, then practice application

There are two distinct phases, and skipping the first undermines the second.

First, build understanding of the laws and frameworks the exam tests: the EU AI ActEU AI ActRegulation (EU) 2024/1689, the European Union's law on artificial intelligence. It takes a risk-based approach: prohibited practices, requirements for high-risk AI systems, transparency obligations for specific uses, and a separate regime for general-purpose AI models. Obligations are divided between providers and deployers. See general-purpose AI, conformity assessment.Open full entry →, the NIST AI RiskriskIn the EU AI Act's terms, the combination of the likelihood that a harm occurs and the severity of it if it does. The link between a principle (via the harm that would breach it) and a control (the measure that reduces it). Naming the harm and assessing its risk is required by Art. 9 before any mitigation measure is chosen. See harm, control, residual risk.Open full entry → Management Framework, ISO/IEC 42001ISO/IEC 42001The international requirements standard for AI management systems, published in 2023 and certifiable. It defines how an organization establishes, implements, maintains, and continually improves a management system for AI. Certification against ISO/IEC 42001 does not create a legal presumption of conformity with the EU AI Act. See AI management system, harmonized standard.Open full entry →, the GDPRGDPRRegulation (EU) 2016/679, the General Data Protection Regulation, the EU's law on the processing of personal data. It applies to AI wherever personal data enters training, inputs, outputs, or logs, and it operates alongside the EU AI Act rather than being replaced by it. See controller, processor, lawful basis, DPIA.Open full entry →, and how they function as governancegovernanceThe system through which an organization steers itself: corporate governance, risk management, compliance, lines of accountability, risk appetite, and the operating model. It exists across everything the organization does, before and beyond AI. AI governance is this same system extended for AI. See AI governance, governance design, execution level.Open full entry → instruments. This is where a structured knowledge base earns its place, because these topics interlock and are easier to learn as a connected system than as isolated facts.

Second, practice scenario-based questions. This is the phase that matters most for the AIGP, because the exam is scenario-based. Good practice questions train you to read a situation, hold several plausible answers in mind, and reason to the one the governance framework supports. Work the explanations, not just the scores: the value is in understanding why the wrong answers are wrong, because that is the reasoning the exam rewards. Avoid relying on question dumps; they teach specific answers, not the transferable judgment the exam tests.

Work from a framework

The reason a framework helps is that the exam is not a set of unrelated facts but a coherent approach to governance. If your preparation connects every topic to a single way of thinking about responsible AIresponsible AIThe set of principles an AI system should live up to: fairness, safety and reliability, privacy, security and robustness, transparency and explainability, accountability, and human oversight. Widely shared and sitting under the EU AI Act and the major frameworks. On their own the principles are statements of intent; the law turns them into duties that cannot be met unless they are carried inside the organization's governance, which is how responsible AI lands in governance rather than beside it. The seven principles are organized into seven pillars, one pillar per principle. See principle, pillar, governance. The seventh principle carries two names in practice: human oversight in the seven-pillar model, and human-centricity in the IAPP AIGP body of knowledge; the substance overlaps.Open full entry →, novel scenarios become tractable, because you can reason from the framework to the answer. If your preparation is a pile of separate facts, every new scenario is a fresh guess. This is why building applied reasoning on a governance framework is more durable than memorization.

Be realistic about time

Plan for 50 to 100 hours of active study, more than the runtime of any video course, even with a strong privacyprivacyThe principle that personal data used by or produced through an AI system stays within the purpose and the legal basis it was collected for. Three routes cause most of the trouble: personal data in training material that was never intended for it, model output that reproduces what the model retained, and purpose creep, where a system built for one use drifts into another the original basis never covered. The GDPR governs this in full, and the EU AI Act adds data governance duties for high-risk systems (Article 10). See DPIA, purpose limitation, responsible AI.Open full entry → or compliance background. Active study means working problems, writing notes, and re-reading what you did not grasp, not passively watching. A common plan is two months at roughly two hours a day, with an intensive final two weeks of practice exams and gap review. Build in rest before exam day; arriving sharp matters more than the last few hours of cramming.

A sample 8-week schedule

A common plan is about two months at roughly two hours a day (50 to 100 hours total). It is a guide, not a rule; adjust it to your background and the domain weightings.

Phase	When	Focus
Map the exam	Start	Download BoK v2.1; note the four domains and their weightings; plan time by weight
Build understanding	Weeks 1-4	The laws and frameworks as one connected system (EU AI Act, NIST AI RMFNIST AI RMFThe AI Risk Management Framework of the US National Institute of Standards and Technology, published as version 1.0 in 2023. It is a voluntary framework built around four functions: govern, map, measure, and manage. In a layered setup, it serves as the risk method inside a management system such as ISO/IEC 42001. See ISO/IEC 42001, ISO/IEC 23894.Open full entry →, ISO/IEC 42001, GDPR), weighted toward the heavier domains
Practice application	Weeks 5-6	Scenario-based questions; work the explanations, not just the score
Final stretch	Weeks 7-8	Practice exams and gap review; rest before exam day

For the full picture of the exam and domains, see the AIGP exam guide.

How GovCompass supports this

The two phases above, build understanding then practice application, are exactly how GovCompass is structured. The free Responsible AI knowledge base builds the understanding of laws and frameworks, organized around the seven pillarspillarA responsible-AI principle as something an organization actively holds rather than merely endorses: one of the seven pillars of responsible AI, one per principle. A pillar is held, not implemented, by naming the harms that would breach the principle, assessing their risk, and placing controls that reduce it. Distinct from agentic AI, which is not one of the seven but a condition that changes how all of them are governed. See principle, harm, risk, agentic AI.Open full entry →. The Academy builds the applied, scenario-based reasoning the exam rewards.

The Academy is built for exactly the two phases above. The AIGP track combines original lessons on the current Body of Knowledge with scenario-based questions that train applied reasoning, and honest readiness tracking so you know when you are truly ready, not just how much you have covered.

Try a real practice question first →

Disclaimer

GovCompass is an independent resource and is not affiliated with, endorsed by, or sponsored by the IAPP. "AIGP" and "IAPP" are trademarks of the IAPP, used here for identification only. Verify exam details against the official BoK at iapp.org.