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| Topic | Details |
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| Topic 1 | - 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 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 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 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 (Q66-Q71):
NEW QUESTION # 66
An app developer is creating a to-do app that compiles users' to-do lists, conducts basic online research about the tasks, prioritizes tasks, and allows users to add descriptions and notes. However, during the app's download process, it requests the user's name, address, phone number, political and union affiliations.
Which privacy principle relevant to the responsible development of AI systems is most likely violated by this data collection practice?
- A. Data portability and subject access rights.
- B. Transparency and explainability.
- C. Data minimization and privacy by design.
- D. Purpose limitation and explicit consent.
Answer: C
Explanation:
Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings. Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design. Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.
NEW QUESTION # 67
What is the primary purpose of conducting ethical red-teaming on an AI system?
- A. To ensure compliance with applicable law.
- B. To identify security vulnerabilities.
- C. To improve the model's accuracy.
- D. To simulate model risk scenarios.
Answer: D
Explanation:
Ethical red-teaming involves simulating adversarial scenarios to uncover risks and vulnerabilities in AI systems before deployment.
NEW QUESTION # 68
An organization that provides AI services to clients has decided to adopt ISO/IEC 42001:2023.
How does the standard define the relationship between the AI policy and the other policies already in place at the organization?
- A. The AI policy should include AI-specific topics that do not intersect with other organizational policies.
- B. The AI policy should address gaps identified from a management review of other organizational policies.
- C. The AI policy should absorb relevant components of other organizational policies.
- D. The AI policy should refer to other organizational policies.
Answer: D
Explanation:
ISO/IEC 42001:2023 establishes an Artificial Intelligence Management System designed to integrate AI governance into an organization ' s existing management structure rather than create an isolated governance regime. Its AI-policy requirements state that the policy should refer, where relevant, to other organizational policies. Therefore, A most accurately describes the relationship. The standard does not require the organization to absorb all related security, privacy, quality, risk, or compliance policies into one AI policy, making B incorrect. C is inconsistent with integrated governance because AI issues commonly intersect with information security, privacy, procurement, data governance, human resources, and enterprise risk management. Management review is an important management-system activity, but D does not state the relationship required between the AI policy and existing policies. ISO/IEC 42001 consequently favors coordinated and mutually aligned organizational governance.
NEW QUESTION # 69
Which of the following is an obligation of an importer of high-risk AI systems under the EU AI Act?
- A. Conduct a data protection impact assessment.
- B. Verify the Declaration of Conformity.
- C. Provide technical documentation.
- D. Affix the CE marking.
Answer: B
Explanation:
Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenot the parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report 2024:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act for importers.
NEW QUESTION # 70
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third-party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because?
- A. It can handle voice commands and is more suitable for phone-based customer support.
- B. It may be better suited for applications requiring customization.
- C. It is less expensive to run
- D. It is built for large-scale, complex dialogues and would be more effective in handling high-volume customer inquiries.
Answer: B
Explanation:
Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs- making it preferable when deep control is needed.
From theAI Governance in Practice Report 2024:
"Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies." (p. 39)
NEW QUESTION # 71
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