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AIGP Artificial Intelligence Governance Professional Exam Topics and Questions

Let's Practice Free IAPP AIGP Questions Aligned with Official Exam Topics

Follows IAPP's official outline Updated 14 Sep, 2026 6 Topics
Reviewed by David Clark, IAPP AIGP Certified Professional
Topic Content

This topic establishes the technical baseline you need to govern what you may not have built yourself. You face definitions, taxonomies and lifecycle stages that sound basic but trip candidates who assume common usage matches the exam's precision. The cost comes when a question hinges on distinguishing supervised from unsupervised learning or identifying which lifecycle phase owns a given control. How Foundations of artificial intelligence is tested Questions present a scenario involving an AI system and ask you to classify...

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The question below asks you to apply lifecycle terminology to a governance activity described in operational terms.

Sample Questions for Topic 1 : Foundations of artificial intelligence
Q1

All of the following areunique characteristics of AIthat require a comprehensive approach to governanceEXCEPT?

Topic Content

Where the first topic dealt with what AI is, this one deals with what it does to people and how principle frameworks respond. You move from taxonomy into consequence and aspiration. The challenge is that every framework uses slightly different language for overlapping ideas, and the exam expects you to recognise the concept behind the label rather than memorising one vendor's list. How AI impacts and responsible principles is tested Items describe a harm, a system characteristic or an ethical...

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What follows turns on distinguishing between two harms that share a symptom but differ in their root cause and remedy.

Sample Questions for Topic 2 : AI impacts and responsible principles
Q2

Which type of existing assessment could best be leveraged to create an Al impact assessment?

Topic Content

Now you apply the principles from topic two to the structures and processes that operationalise them. This topic is where governance moves from aspiration to implementation. You need to know what an AI governance function looks like, how risk identification differs from risk assessment, and which frameworks and standards exist to scaffold the work. Candidates often underestimate the specificity required when naming a framework or describing the sequencing of risk activities. How AI governance and risk management is tested Questions...

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The scenario that follows requires you to select the governance structure that addresses a described gap, not the one that sounds most comprehensive.

Topic Content

This is the heaviest topic by question count and the one where precision matters most. You face AI-specific regulation, existing laws that now apply to AI systems, the points where GDPR intersects with algorithmic processing, intellectual property questions raised by training data and generated output, and liability reform proposals. Each sub-topic has its own vocabulary and its own traps. โ€“ AI-specific regulation and GDPR intersections The EU AI Act is the primary AI-specific instrument you need to know, including its...

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The item below asks you to apply a legal rule to a fact pattern where two regimes appear relevant but only one governs the issue described.

Topic Content

Development is where technical choices become governance obligations. This topic covers how you govern design and development decisions, and how you govern the collection and use of data for training and testing. Both sub-topics demand that you connect lifecycle activities from topic one to the controls and principles from topics two and three, then apply the legal constraints from topic four. How Governing AI development is tested Questions describe a development activity or a data practice and ask you to...

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The scenario that follows describes a data practice during training and asks you to identify the governance control that addresses the risk created.

Topic Content

Deployment is the point of no return. This topic covers the factors and risks that inform the decision to deploy, the activities that assess whether the model is ready, and the governance that applies once the system is live. You are tested on whether you can distinguish pre-deployment assessment from post-deployment monitoring, and whether you know what triggers a decision to halt or withdraw. How Governing AI deployment is tested Questions present a deployment decision, a model assessment activity or...

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What follows asks you to identify the missing assessment activity that should precede deployment, given the system characteristics and intended use described.

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