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IAPP AIGP Exam Questions
- Topic 1: Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
- Topic 2: Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
- Topic 3: Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.
- Topic 4: Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
Free IAPP AIGP Exam Actual Questions
Note: Premium Questions for AIGP were last updated On Oct. 26, 2026 (see below)
A company has developed a proprietary AI model that analyzes consumer online behavior and predicts what prices consumers would be willing to pay for certain products, so that retailers may modify pricing accordingly. To test the model, the company has:
Performed an impact assessment
Conducted repeatability tests
Exposed the model to edge cases and potential malicious input
Conducted adversarial testing to identify security threats
Assessed and mitigated discrimination risks
Which additional responsible AI principle has the company failed to assess?
The correct answer is D because the company has already addressed several key responsible AI principles, including fairness through discrimination risk mitigation, robustness through adversarial and edge-case testing, and reliability via repeatability testing. However, there is no indication that the company has addressed transparency, which involves providing clear information about how the model operates, how pricing decisions are made, and how outputs may affect consumers. Transparency is a critical AI governance principle, especially in consumer-facing applications like dynamic pricing, where decisions can significantly impact individuals. Governance frameworks emphasize that users and stakeholders should be informed about AI-driven decisions and their implications. Without transparency, even technically sound and fair systems may lack trustworthiness and fail to meet regulatory or ethical expectations.
All of the following are potential benefits of using private over public LLMs EXCEPT?
Private LLMs offer advantages likecustomizability,reduced hallucination,confidentiality, andalignment with enterprise-specific tasks, but theydo not inherently reduce the time or effortneeded fordata validation or verification--- which remains an essential step regardless of model privacy.
From the AI risk and quality sections:
''Ensuring the quality of the data... is highly contextual and must be validated regardless of the model's deployment environment.'' (p. 17)
B, C, Dare legitimate benefits of private LLMs.
Ais incorrect --- validation still requires time and resources.
What is the most important reason to document the results of AI testing?
Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and compliance. This is central to enabling audits, investigations, or regulatory inquiries into the system's development and performance.
From theAI Governance in Practice Report 2025:
''Documentation and recordkeeping are essential components... to demonstrate AI system compliance, trace system behavior, and support audits and conformity assessments.'' (p. 34--35)
''Maintaining audit trails across development and deployment enables transparency and accountability.'' (p. 12)
AandBare benefits, but not theprimary governance justification.
D-- Limiting future testing is not a recommended goal.
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions. One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which other stakeholder groups should be involved in the selection and implementation of the Al hiring tool?
In the selection and implementation of the AI hiring tool, involving Finance and Legal is crucial. The Finance team is essential for assessing cost implications, budget considerations, and financial risks. The Legal team is necessary to ensure compliance with applicable laws and regulations, including those related to data privacy, employment, and anti-discrimination. Involving these stakeholders ensures a comprehensive evaluation of both the financial viability and legal compliance of the AI tool, mitigating potential risks and aligning with organizational objectives and regulatory requirements.
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
The data processed by the AI system would be classified as:
The correct answer is D. Key stroke dynamics, used to identify individuals, fall under biometric data, which is a special category of personal data under the GDPR and other frameworks.
From the AI Governance in Practice Report 2025:
''Keystroke dynamics may constitute biometric data if used to uniquely identify an individual... Biometric data is classified as special category personal data and requires higher protection standards.''
Also reflected in ILT Participant Guide:
''Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals.''
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