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| 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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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| 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.
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q32-Q37):
NEW QUESTION # 32
What is the best reason for a company to adopt a policy that prohibits the use of generative AI?
- A. To avoid using technology that cannot be monetized.
- B. To avoid needing to identify and hire qualified resources.
- C. To avoid accidental disclosure of its confidential and proprietary information.
- D. To avoid the time necessary to train employees on acceptable use.
Answer: C
Explanation:
The correct answer is D because one of the most significant risks associated with generative AI is the potential leakage of sensitive, confidential, or proprietary information. AI governance frameworks emphasize data protection, confidentiality, and secure data handling as critical organizational responsibilities. When employees input sensitive data into generative AI tools, especially external or third-party systems, that information may be stored, reused, or exposed unintentionally. This creates legal, regulatory, and reputational risks. As a result, organizations may restrict or prohibit generative AI use to prevent unauthorized data disclosure. The other options relate to operational or business considerations, which are not primary governance drivers. Protecting sensitive information aligns with core AI governance principles such as privacy, security, and risk mitigation, making it the strongest justification for such a policy.
NEW QUESTION # 33
What is most likely the first action that a developer takes to map, plan and scope an AI project?
- A. Perform an algorithmic impact assessment leveraging PIAs.
- B. Determine feasibility and optionality of redress.
- C. Use a test, evaluation, verification, validation (TEVV) process.
- D. Define the business case and perform a cost/benefit analysis answering the question of "why AI?"
Answer: D
Explanation:
The first action is to clearly define the business case and conduct a cost/benefit analysis to justify the use of AI and set project scope.
NEW QUESTION # 34
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
- A. Ensure the vendor provides indemnification for the AI tool
- B. Continue to require the company's hiring personnel to manually screen all applicants
- C. Require the procurement and deployment teams to agree upon the AI tool
- D. Test the AI tool pre- and post-deployment
Answer: D
Explanation:
The correct answer is A. Pre- and post-deployment testing ensures bias, accuracy, and fairness are evaluated and corrected as needed, which is essential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report 2024 (Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).
NEW QUESTION # 35
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?
- A. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
- B. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
- C. Implementing a formal incident response plan to address incidents that may occur during system operation.
- D. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.
Answer: A
Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remaininternal or shared with regulators.
From theAI Governance in Practice Report2025:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)
NEW QUESTION # 36
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?
- A. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
- B. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
- C. Implementing a formal incident response plan to address incidents that may occur during system operation.
- D. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.
Answer: A
Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remain internal or shared with regulators.
From theAI Governance in Practice Report 2024:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)
NEW QUESTION # 37
......
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