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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 3	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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q177-Q182):

NEW QUESTION # 177
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

A. The data aggregator.
B. The marketing agency ' s client.
C. The marketing agency.
D. The tech company.

Answer: A

Explanation:
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report 2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data ' s lawful collection by the databroker..." (p. 19) This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

NEW QUESTION # 178
Which of the following considerations is the most important in mitigating the potential of bias in training and testing data?

A. Receiving explicit consent to use the data.
B. Validating that the data is representative.
C. Using privacy-enhancing tools to protect the data.
D. Assessing the sufficiency of the third-party data.

Answer: B

Explanation:
Ensuring that training and testing data is representative of the real-world population is critical to mitigating bias in AI models.

NEW QUESTION # 179
What is the primary purpose of an AI impact assessment?

A. To identify and measure the benefits of an AI system.
B. To determine whether a conformity assessment is needed.
C. To escalate the findings to the appropriate owner(s).
D. To anticipate and manage the potential risks and harms of an AI system.

Answer: D

Explanation:
The primary purpose of an AI impact assessment is to proactively anticipate, identify, and manage potential risks and harms associated with the deployment of an AI system.

NEW QUESTION # 180
A company is working to develop a self-driving car that can independently decide the appropriate route to take the driver after the driver provides an address. If they want to make this self-driving car "strong" AI, as opposed to "weak," the engineers would also need to ensure?

A. That the AI has strong cybersecurity to prevent malicious actors from taking control of the car.
B. That the AI has full human cognitive abilities that can independently decide where to take the driver.
C. That they have obtained appropriate intellectual property (IP) licenses to use data for training the AI.
D. That the AI can differentiate among ethnic backgrounds of pedestrians.

Answer: B

Explanation:
Strong AI refers to artificial intelligence with full human cognitive abilities, capable of independent reasoning and decision-making beyond specific tasks like route selection.

NEW QUESTION # 181
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Which of the following PETs would be effective in developing AI systems?

A. Data mapping.
B. Data cleansing.
C. Data minimization.
D. Federated learning.
E. Anonymization.

Answer: C,D,E

Explanation:
A, B, and E are the intended privacy-preserving choices. Data minimization limits processing to information that is adequate, relevant, and necessary for the defined purpose, reducing privacy exposure during AI development. Anonymization protects individuals by transforming information so that people are no longer identifiable under the applicable standard. Federated learning is a recognized privacy-enhancing technology that enables models to be trained across distributed datasets without centrally sharing the underlying raw training data. The ICO specifically identifies federated learning and related privacy-preserving methods as useful during AI training and explains the relationship between anonymization and data minimization. Data mapping is primarily a governance and inventory activity used to understand data flows, while data cleansing principally improves data quality by correcting or removing inaccurate, incomplete, or unsuitable records; neither is inherently a PET.

NEW QUESTION # 182
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