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IAPP AIGP Exam Syllabus Topics:

Section	Objectives
AI Risk Management	- Operationalizing Risk Management 1. Risk identification and assessment 2. Risk mitigation strategies - Risk Frameworks and Standards 1. ISO 31000 Risk Management 2. NIST AI Risk Management Framework (RMF)
AI Laws, Regulations, and Standards	- Global AI Frameworks 1. OECD AI Principles 2. ISO/IEC Standards 3. UNESCO AI Ethics - Regional and National Regulations 1. National AI Strategies 2. EU AI Act 3. US State and Federal Regulations
Foundations of AI Governance	- Impacts of AI 1. Risks of AI 2. Benefits of AI - AI Governance Fundamentals 1. Key AI Terminology and Concepts 2. Defining AI and its different approaches 3. Understanding the AI Development Life Cycle
AI Governance Management	- Life Cycle Management 1. Auditing and Monitoring 2. Third-party management 3. Incident Management - Emerging Issues 1. Generative AI 2. Future Trends 3. Ethical AI
AI Governance Implementation	- Data Governance for AI 1. Data Privacy and Protection 2. Data Quality and Integrity - Organizational Governance 1. Policies and Procedures 2. Roles and Responsibilities (RACI) 3. Governance Structures

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
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. Implementing a formal incident response plan to address incidents that may occur during system operation.
B. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
C. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
D. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.

Answer: C

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 # 60
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization's AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:

A. Offer a regulatory sandbox for risk reporting
B. Provide a suite of resources to manage risks
C. Promote interoperability across AI systems
D. Pilot new standards for AI red-teaming

Answer: B

Explanation:
The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.
From the AIGP ILT Guide - U.S. Risk Frameworks Module:
"NIST's ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use." Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):
"ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications." ARIA is not a red-teaming or sandbox program-it's an assessment and governance resource.

NEW QUESTION # 61
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.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?

A. Create a bias bounty program.
B. Perform an impact assessment.
C. Engage a third party to perform an audit.
D. Procure more data from clinical research partners.

Answer: B

Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

NEW QUESTION # 62
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

A. The contact details of the data protection officer and the data protection national authority.
B. The personal data used during processing, including inferences drawn by the Al system about the data.
C. The existence of automated decision-making and meaningful information on its logic and consequences.
D. The data protection impact assessments carried out on the Al system and legal bases for processing.

Answer: C

Explanation:
The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.

NEW QUESTION # 63
Which of the following is a foundational characteristic of effective AI governance?

A. Thorough reviews of a company's public filings with experts
B. Uniform policies and procedures across developer, deployer and user roles
C. Engagement of a cross-functional team
D. Reliance on tested vendor management processes

Answer: C

Explanation:
The correct answer is Engagement of a cross-functional team. Effective AI governance requires collaboration among various organizational functions including legal, compliance, IT, ethics, and data science.
From the AIGP Body of Knowledge:
"AI governance cannot be siloed-it requires input and oversight from across departments... A cross- functional team ensures that ethical, technical, legal, and operational risks are all appropriately managed." Also confirmed in the ILT Participant Guide:
"Cross-functional teams allow organizations to bring in different perspectives... Legal, compliance, and technical experts must work together to ensure responsible AI outcomes."

NEW QUESTION # 64
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