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Exam Information
Official specifications published by IAPP
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Skills this practice test covers
- Apply the common principles of responsible AI to a proposed AI use case.
- Classify an AI system under the EU AI Act's risk levels and list the duties that follow.
- Apply notice, consent and data minimization rules to the data used to train a model.
- Govern data lineage, testing and release checks for a model that is still being built.
- Review vendor and open source agreements before a third-party model goes into use.
- Set monitoring, incident handling and deactivation rules for a model in production.
Explore related topics
AIGP exam content
4 domains · 44 topics · 226 of 234 questions sorted by domain
Understanding the foundations of AI governance[object Object]%54 questions
This domain explains what AI is, why it needs its own governance, and how an organization sets that governance up. It covers responsible AI principles, roles across teams, training, and the policies that apply throughout the AI life cycle.
Skills tested
- Separate the types of AI and name the risks and harms each can cause.
- Explain the traits that make AI hard to govern, such as complexity, opacity and brittleness.
- Apply responsible AI principles such as fairness, accountability and human oversight.
- Assign governance roles to developers, deployers and users, and set up cross-functional work.
- Write oversight, privacy, security and third-party risk policies for the AI life cycle.
Questions by topic
- Establish and communicate organizational expectations for AI governance4
- Understand what AI is and why it needs governance2
- Identify and apply the common principles of responsible AI14
- Identify the unique characteristics of AI that require a comprehensive approach to governance9
- Establish policies and procedures to apply throughout the AI life cycle5
- Create and deliver a training and awareness program to all stakeholders4
- Identify differences among AI developers, deployers and users from a governance perspective4
- Establish cross-functional collaboration in the AI governance program4
- Differentiate approaches to AI governance based upon company size, maturity, industry, products and services, objectives and risk tolerance.3
- Evaluate and update existing data privacy and security policies for AI4
- Identify the types of risks and harms posed by AI to individuals1
Understanding how laws, standards and frameworks apply to AI[object Object]%66 questions
AI already falls under many laws that were not written for it. Candidates apply privacy, non-discrimination, consumer protection and product liability law to AI, then learn the EU AI Act and the main standards from the OECD, NIST and ISO.
Skills tested
- Apply notice, choice, consent and data minimization to the way AI uses data.
- Spot discrimination risks in uses such as employment, housing, and credit and lending.
- Classify a system under the EU AI Act and list the requirements for its risk level.
- Describe the distinct requirements for general purpose AI models and the Act's penalties.
- Use the NIST AI Risk Management Framework and the core ISO AI standards.
Questions by topic
- Understand the main elements of the EU AI Act19
- Understand the main industry standards and tools that apply to AI11
- Understand how existing data privacy laws apply to AI7
- Understand how other types of existing laws apply to AI4
- Understand how consumer protection laws apply to AI4
- Understand how product liability laws apply to AI4
- Understand how intellectual property laws apply to AI3
- Understand the distinct requirements for general purpose AI models3
- Understand the NIST AI Risk Management Framework and Playbook5
- Understand the core ISO AI standards3
- Understand how non-discrimination laws apply to AI2
- Understand the risk classification framework for AI1
Understanding how to govern AI development[object Object]%60 questions
This domain follows a model while it is built. Candidates define the use case, run an impact assessment, govern the training and testing data, and decide when the model is ready for release.
Skills tested
- Define the business context and use case before design work starts.
- Perform or review an impact assessment and identify the laws that apply.
- Establish data governance, and document data lineage and provenance.
- Plan bias, interpretability and integration testing, and track the issues found.
- Prepare a model for release and set its monitoring and maintenance schedule.
Questions by topic
- Govern the collection and use of data in training and testing the AI model10
- Govern the designing and building of the AI model11
- Govern the release, monitoring and maintenance of the AI model4
- Collaborate with cross-functional stakeholders to understand why incidents arise from AI models7
- Conduct periodic activities to assess the AI model's performance, reliability and safety9
- Document the training and testing process5
- Identify and manage issues and risks during training and testing of an the AI model4
- Define the business context and use case of the AI model3
- Assess readiness and prepare for release into production2
- Establish and document data lineage and provenance3
- Document incidents, issues, risks and post-market monitoring plans1
- Identify and manage the internal and external risks and contributing factors related to designing and building the AI model1
Understanding how to govern AI deployment and use[object Object]%46 questions
The largest domain covers the decision to deploy and everything after it. Candidates weigh model types and deployment options, review agreements, monitor the model in use, and plan for downstream harms and deactivation.
Skills tested
- Compare classic and generative AI, and cloud, on-premise and edge deployment.
- Evaluate key terms and risks in vendor and open source agreements.
- Address the issues that are unique to deploying a proprietary model.
- Forecast secondary uses and downstream harms, and reduce them.
- Write a policy to deactivate or localize a model when needed.
Questions by topic
- Evaluate key factors and risks relevant to the decision to deploy the AI model5
- Govern the deployment and use of the AI model11
- Perform key activities to assess the AI model5
- Understand the differences in AI deployment options7
- Create and implement a policy and controls to deactivate or localize an AI model as necessary6
- Forecast and reduce risks of secondary or unintended uses and downstream harms5
- Identify and evaluate key terms and risks in the vendor or open source agreement4
- Document incidents, issues, risks and post-market monitoring plans1
- Identify and understand issues that are unique to a company deploying its own proprietary AI model2
Requirements
- No formal prerequisites: anyone can book the exam.
- Recommended: work experience in privacy, data protection or AI governance.
- Recommended: an understanding of AI technologies and how they are applied.
- Recommended: familiarity with regulatory compliance and the principles of risk management.
About the AIGP exam
AIGP is the IAPP exam on keeping AI lawful and accountable at every stage, from choosing a use case to switching a model off. It is written for privacy, compliance, risk and legal professionals, and for the product and technology leaders who work beside them. The four domains move from governance foundations, through the laws and standards that apply, to governing development and then deployment. It leads to the Artificial Intelligence Governance Professional certification, which is valid for two years.
The domain on laws, standards and frameworks has the biggest share of the PlanetCert practice test, and the EU AI Act is its largest single topic. A developer's duty and a deployer's duty often sound the same, so the explanations spell out which role carries each one. In Flashcard Mode, the OECD Principles, the NIST framework and ISO 42001 can be drilled until each is easy to tell apart. For a use case that seems to sit between two EU AI Act risk levels, Brainy AI Assistant can explain which level applies and why.
How to earn the certification
- Earns
- Artificial Intelligence Governance Professional (AIGP)Professional
The AIGP certification is valid for two years.
- After
- FIP Fellow of Information Privacy (FIP)
The IAPP's fellow-level designation, listed as the next step on the AI governance track.
- FIP Fellow of Information Privacy (FIP)
How to prepare for AIGP
- 1
Learn the AI basics behind the rules
Start with the foundations domain: AI types, the harms they can cause, and why traits such as opacity and brittleness need governance. Later questions assume this base.
- 2
Sort use cases by EU AI Act risk
List several real AI uses, from a spam filter to a hiring tool, and place each one at a risk level. Then write the duties that follow for the developer and the deployer.
- 3
Put the standards side by side
Lay out the OECD Principles, the NIST AI Risk Management Framework and ISO 42001 in one table. Note what each asks an organization to do and where they overlap.
- 4
Give deployment and use the most time
It is the largest domain on the exam. Practise reviewing a vendor agreement, choosing between cloud, on-premise and edge deployment, and writing a deactivation policy.
- 5
Test the whole life cycle under time
Do a Timed Mode run of the AIGP practice test, then sort each miss by its stage of the AI life cycle. Brainy AI Assistant can explain the rule behind each one.
Start a timed AIGPrun
Who AIGP is for
- Data protection officers and privacy teams who now review new AI projects.
- Risk and compliance managers who bring AI systems into their control programs.
- AI product managers and technology leaders who need governance built into each release.
- Legal and ethics professionals who advise on whether an AI use is lawful and fair.
Typical job titles: AI governance manager · Privacy officer · Data protection officer · Compliance officer · Risk manager · AI product manager
AIGP glossary
17 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- AI Risk Management Framework
- NIST's voluntary framework for managing AI risk, built on four core functions: Govern, Map, Measure and Manage.
- Brittleness
- The way an AI model can fail badly when its inputs differ only a little from what it saw in training.
- Conformity assessment
- The check that a high-risk AI system meets the EU AI Act's requirements before it is placed on the market.
- Data Lineage and Provenance
- The record of where training data came from, the rights to use it, and how it changed on its way into the model.
- Deployer obligations
- The duties of the organization that puts an AI system to use, as opposed to the developer that built it.
- EU AI Act
- The European Union law that sorts AI systems by risk level and sets duties for the organizations that build, sell and use them.
- Fine-tuning
- Training an existing model further on a smaller, focused data set, so that it fits one task.
- General Purpose AI Models
- Models trained on broad data that can do many different tasks. The EU AI Act gives them their own set of requirements.
- High-risk AI systems
- AI systems that the EU AI Act allows only with strict controls, such as risk management, documentation, human oversight and a conformity assessment.
- Human oversight
- The ability of a person to monitor an AI system, understand its output, and step in or stop it.
- ISO 42001
- The international standard for an AI management system: the policies, roles and processes an organization uses to govern its AI.
- Limited-risk AI systems
- AI systems that carry mainly transparency duties, such as telling people that they are dealing with AI.
- Model card
- A short document that describes a model's purpose, training data, performance and limits for the people who use it.
- Model drift
- A fall in a model's accuracy over time, because the data it meets in use moves away from the data it was trained on.
- OECD Principles
- The OECD's principles for trustworthy AI, which many governments use as a shared starting point for AI policy.
- Post-market monitoring
- Tracking an AI system's performance and incidents after its release, so that problems are found and reported.
- Privacy by Design
- Building privacy protections into a system from the start, instead of adding them after it is built.