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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Exam Duration:	150 minutes
Certificate Validity Period:	2 Years
Exam Price:	USD 550
Related Certifications:	CIPP/US CIPP/A CIPP/E CIPM CIPT
Passing Score:	300
Real Exam Qty:	100
Available Languages:	English
Exam Format:	Multiple-choice
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online (OnVUE) or Test Center (Pearson VUE)
Pre Condition:	No specific prerequisites. Recommended background in privacy, compliance, legal, or technology.
Official Syllabus URL:	https://iapp.org/certify/aigp/

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	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 4	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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q140-Q145):

NEW QUESTION # 140
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?

A. Statistical sampling.
B. Adversarial robustness.
C. Risk probability/severity.
D. Decision analysis.

Answer: C

Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance.
Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

NEW QUESTION # 141
All of the following issues are unique for proprietary AI model deployments EXCEPT?

A. The necessity of performing conformity assessments.
B. The acquisition of training data.
C. The cost of AI chips.
D. The potential for bias.

Answer: D

Explanation:
Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems - regardless of origin - require evaluation for fairness, accuracy, and representativeness.
From theAI Governance in Practice Report 2024:
"Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced." (p. 41)

NEW QUESTION # 142
Which of the following deployments of generative Al best respects intellectual property rights?

A. The system produces content that includes trademarks and copyrights.
B. The system provides attribution to creators of publicly available information.
C. The system produces content that is modified to closely resemble copyrightedwork.
D. The system categorizes and applies filters to content based on licensing terms.

Answer: D

Explanation:
Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.

NEW QUESTION # 143
A French medical research center wishes to develop an AI-based system that will predict the risk of serious diseases based on patients ' genetic data. To do so, it contracts with a technology company and provides it with patients ' data previously obtained by the center during research.
To guarantee compliance when processing special categories of personal data, the medical research center must ensure that?

A. The patients have given explicit consent to using the data.
B. The technology company is located in the EU and is not cloud-based.
C. The AI-based system is designed for the purposes of preventive medicine.
D. The patients ' health and genetic data is anonymized.

Answer: A

Explanation:
Genetic and health information are special categories of personal data under Article 9 of the GDPR, for which processing is generally prohibited unless a valid Article 9 exception applies. Among the available answers, explicit consent provides the clearest applicable condition: Article 9(2)(a) permits processing where the data subject has explicitly consented to processing for specified purposes. Importantly, explicit consent is not the only possible legal condition under the GDPR; scientific research and healthcare processing may sometimes rely on other Article 9 conditions where applicable law and safeguards permit. However, A merely describing a preventive-medicine purpose does not itself establish the necessary legal condition. D is irrelevant because EU establishment and non-cloud processing do not establish lawfulness. B would materially change the analysis only if the data were genuinely anonymized so that they were no longer personal data.

NEW QUESTION # 144
When assessing the success of an AI system in meeting its objectives, which of the following approaches best aligns with the requirement to ensure a comprehensive evaluation while avoiding automation bias?

A. Rely on the AI system's output to determine if the goals were achieved as this will provide a reliable and objective measure.
B. Focus on user feedback about the AI system's performance to directly measure the system's effectiveness in meeting the objectives.
C. Evaluate and align pre-defined benchmarks which will provide evidence of having achieved your system goals.
D. Conduct a thorough review that includes the AI system's input and output data, human interpretation, and potential secondary or unintended outputs.

Answer: D

NEW QUESTION # 145
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