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IAPP AIGP Exam Syllabus Topics:

SectionObjectives
Topic 1: Foundations of AI Governance- Impacts of AI
  • 1. Benefits of AI
  • 2. Risks of AI
- AI Governance Fundamentals
  • 1. Understanding the AI Development Life Cycle
  • 2. Defining AI and its different approaches
  • 3. Key AI Terminology and Concepts
Topic 2: AI Governance Management- Life Cycle Management
  • 1. Third-party management
  • 2. Auditing and Monitoring
  • 3. Incident Management
- Emerging Issues
  • 1. Generative AI
  • 2. Future Trends
  • 3. Ethical AI
Topic 3: AI Risk Management- Risk Frameworks and Standards
  • 1. NIST AI Risk Management Framework (RMF)
  • 2. ISO 31000 Risk Management
- Operationalizing Risk Management
  • 1. Risk identification and assessment
  • 2. Risk mitigation strategies
Topic 4: 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. EU AI Act
  • 2. US State and Federal Regulations
  • 3. National AI Strategies
Topic 5: AI Governance Implementation- Organizational Governance
  • 1. Policies and Procedures
  • 2. Governance Structures
  • 3. Roles and Responsibilities (RACI)
- Data Governance for AI
  • 1. Data Privacy and Protection
  • 2. Data Quality and Integrity

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q122-Q127):

NEW QUESTION # 122
What is the technique to remove the effects of improperly used data from an ML system?

Answer: A

Explanation:
Model disgorgement is the technique used to remove the effects of improperly used data from an ML system.
This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.


NEW QUESTION # 123
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company ' s core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following best exemplifies human oversight capabilities you should enable under the relevant AI laws?

Answer: D

Explanation:
The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act.
Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize "human-in-the-loop" controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.


NEW QUESTION # 124
A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent. Which is the best step the company should take to address this development?

Answer: A

Explanation:
Temporarily deactivating the model prevents further operational issues and allows a thorough assessment and retraining to reduce false positives before redeployment.


NEW QUESTION # 125
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

Answer: B

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.


NEW QUESTION # 126
What type of organizational risk is associated with Al's resource-intensive computing demands?

Answer: B

Explanation:
AI's resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.


NEW QUESTION # 127
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