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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q148-Q153):

NEW QUESTION # 148
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. Continue to require the company's hiring personnel to manually screen all applicants
B. Require the procurement and deployment teams to agree upon the AI tool
C. Test the AI tool pre- and post-deployment
D. Ensure the vendor provides indemnification for the AI tool

Answer: C

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 # 149
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?

A. To detect an individual's intent for law enforcement purposes.
B. To implement social scoring.
C. To manage border control.
D. To promote equitable distribution of welfare benefits.

Answer: B

Explanation:
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.

NEW QUESTION # 150
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

A. To minimize degradation in prediction accuracy due tochanges in data.
B. To ensure interpretability of the model's predictions.
C. Account for new interpretations of the same data.
D. Adjust the model's hyper parameters specific use case.

Answer: B

Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

NEW QUESTION # 151
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?

A. Evaluate the country of origin of the data.
B. Ensure the data is labeled and formatted.
C. Apply privacy-enhancing technologies to the data.
D. Identify fits and gaps in the combined data.

Answer: D

Explanation:
In the design phase of integrating data from different sources, identifying fits and gaps is crucial. This process involves understanding how well the data from the clinical research partner aligns with the healthcare network's existing data. It ensures that the combined data set is coherent and can be effectively used for training the AI algorithm. This step helps in spotting any discrepancies, inconsistencies, or missing data that might affect the performance and accuracy of the AI model. It directly addresses the integrity and compatibility of the data, which is foundational before applying any privacy-enhancing technologies, labeling, or evaluating the origin of the data. Reference: AIGP Body of Knowledge on Data Integration and Quality.

NEW QUESTION # 152
A shipping service based in the US is looking to expand its operations into the EU. It utilizes an in-house developed multimodal AI model that analyzes all personal data collected from shipping senders and recipients, and optimizes shipping routes and schedules based on this data.
As they expand into the EU, all of the following descriptions should be included in the technical documentation for their AI model EXCEPT?

A. A detailed description of the elements of the AI system and of the process for its development.
B. A description of the appropriateness of the performance metrics for the specific AI system.
C. A general description of the AI system.
D. A description of the prioritization of the risks of deployment of the AI system.

Answer: D

Explanation:
The EU AI Act outlines what must be included intechnical documentationfor high-risk systems. These requirements are designed to supportconformity assessment, transparency, and traceability.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation... must include a general description of the AI system, the intended purpose, and a detailed description of the elements and development process." (p. 34)
"Documentation... includes training, testing, evaluation procedures, andappropriateness of performance metrics." (p. 34-35) Therisk management systemis addressed separately through arisk management plan, not within the technical documentation itself.
Thus:
* A, C, and Dare explicitly required in thetechnical documentation.
* B, while important, is part of therisk management process, not a required section oftechnical documentation.

NEW QUESTION # 153
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