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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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.
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.
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 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 (Q21-Q26):

NEW QUESTION # 21
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?

A. Forgetting.
B. Hallucination.
C. Uncertainty.
D. Brittleness.

Answer: D

Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

NEW QUESTION # 22
ISO 42001 International Standard offers guidance for organizations to develop trustworthy AI management systems by:

A. Requiring specific minimum parameters for key suppliers and key aspects of AI management systems.
B. Requiring organizations to continuously improve the effectiveness of their AI management systems.
C. Explicitly over-riding previously issued and now outdated ISO standards.
D. Focusing on high-risk aspects of development of AI management systems.

Answer: B

NEW QUESTION # 23
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. Purpose limitation and explicit consent.
B. Data portability and subject access rights.
C. Transparency and explainability.
D. Data minimization and privacy by design.

Answer: D

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 # 24
Which of the following use cases would be best served by a non-AI solution?

A. An e-commerce provider wants to make personalized recommendations.
B. A customer service agency wants to automate answers to common questions.
C. A non-profit wants to develop a social media presence.
D. A business analyst wants to develop advertising campaigns.

Answer: C

NEW QUESTION # 25
What is a primary liability concern when a company deploys a proprietary Agentic AI model?

A. Dependence on open-source software for orchestration.
B. Direct accountability for harmful or deceptive agent behavior.
C. Lack of available APIs for memory integration.
D. Greater public scrutiny due to external model hosting.

Answer: B

Explanation:
The correct answer is A because deploying a proprietary agentic AI model places direct responsibility on the organization for the actions and outcomes of that system. AI governance frameworks emphasize accountability as a core principle, meaning organizations must take responsibility for harms caused by their AI systems, including misleading, biased, or unsafe outputs. Agentic AI systems, which can act autonomously and make decisions, introduce heightened liability risks because their behavior may directly impact users or third parties. Regulatory approaches increasingly focus on ensuring that deployers cannot shift responsibility, especially when the system is proprietary and under their control. The other options relate to operational or technical considerations, not primary legal liability. Ensuring accountability for AI behavior is central to compliance, risk management, and maintaining trust.

NEW QUESTION # 26
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