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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 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 3	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 4	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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
CASE STUDY
Please use the following to answer the next question:
A small local flower delivery company operates a few stores and has a limited IT budget. To reduce the time spent on customer service, which is a major drain on employee time, the company plans to implement a simple but high-performance generative AI chatbot on its website. The chatbot will provide real-time, fact-based responses to customer inquiries and assist with order processing, helping to reduce incoming phone calls.
The company currently uses a single on-premises server for its order database and email system and has not yet partnered with a cloud provider.
An internal data privacy policy governs the use of customer dat
a. This policy, written before the adoption of AI, allows the use or transfer of customer data beyond name and contact information but requires that all such data be deleted after the order is completed. The company prefers to maintain this policy without changes.
What deployment option is the simplest, quickest, and most cost-effective option across the various potential AI models for this particular company?

A. Software-as-a-Service (SaaS) accessed via an API.
B. On-premises, running on the company's existing server.
C. Hosting on rented servers via a private cloud.
D. Edge computing running on each customer's hardware.

Answer: A

Explanation:
Software-as-a-Service accessed through an API is the most practical deployment approach for a small organization with limited IT infrastructure and budget. SaaS allows the company to consume a provider-managed application without purchasing and maintaining the substantial computing infrastructure normally needed for modern generative AI. Under NIST's cloud-computing model, SaaS consumers use provider applications while the provider manages the underlying network, servers, operating systems, storage, and much of the supporting infrastructure. This makes implementation comparatively rapid and scalable. Running the model on the existing on-premises server could require hardware upgrades and specialized maintenance. A private-cloud deployment introduces additional infrastructure and administrative complexity. Edge deployment on customers' devices would also create substantial technical and compatibility challenges. Therefore, considering the company's resources and desired speed of deployment, C is the strongest option.

NEW QUESTION # 16
What is most likely the first action that a developer takes to map, plan and scope an AI project?

A. Determine feasibility and optionality of redress
B. Use a test, evaluation, verification, validation TEVV process
C. Perform an algorithmic impact assessment leveraging PIAs
D. Define the business case and perform a cost benefit analysis answering the question of why AI

Answer: D

Explanation:
The correct answer is A because the first step in any AI project lifecycle is to clearly define the business objective and justify the use of AI. AI governance frameworks emphasize that planning begins with understanding the purpose, value, and necessity of the system before moving into design or risk assessment stages. Establishing a business case ensures alignment with organizational goals, identifies expected benefits, and evaluates whether AI is the appropriate solution. This step corresponds to the planning phase of the AI lifecycle, where objectives and intended outcomes are documented. In contrast, TEVV processes occur during development and testing, while impact assessments and redress considerations arise later as part of risk management and governance. Starting with a clear "why AI" foundation ensures responsible, efficient, and goal-oriented system development.

NEW QUESTION # 17
All of the following are compliance considerations specific to third-party governance when using GenAI models EXCEPT?

A. Responsibility and liability between the company and vendor.
B. Heightened cybersecurity risks.
C. Visibility into the details of a third-party model.
D. Back testing of the model.

Answer: D

Explanation:
Third-party AI governance introduces issues that arise specifically because an organization depends on an external model provider. These include limited visibility into model architecture, training sources, capabilities and limitations; cybersecurity and information-security risks created through external integrations; and determining contractual responsibility and liability between the organization and vendor. These considerations make A, C, and D directly relevant to third-party governance. Back testing, by contrast, is primarily a model testing or validation methodology rather than a compliance consideration specific to the third-party relationship. Testing third-party AI systems remains an important governance practice, but the term " back testing " itself describes performance evaluation rather than vendor governance. NIST guidance emphasizes third-party transparency, due diligence, security, procurement controls, contractual expectations, and continuing risk management when organizations acquire or integrate external AI systems.

NEW QUESTION # 18
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" Al, as opposed to "weak," the engineers would also need to ensure?

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

Answer: D

Explanation:
Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities. For the self-driving car to be classified as "strong" AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference: AIGP BODY OF KNOWLEDGE and AI classifications.

NEW QUESTION # 19
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

A. Define suitable measures to safeguard personal data.
B. Provide a right to review automated decision.
C. Establish a human-in-the-loop procedure.
D. Provide a just-in-time notice about the automated decision-making logic.

Answer: C

Explanation:
The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in-time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals' rights.

NEW QUESTION # 20
......

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