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IAPP AIGP Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Governance Management	- Life Cycle Management 1. Incident Management 2. Third-party management 3. Auditing and Monitoring - Emerging Issues 1. Future Trends 2. Ethical AI 3. Generative AI
Topic 2: Foundations of AI Governance	- AI Governance Fundamentals 1. Defining AI and its different approaches 2. Key AI Terminology and Concepts 3. Understanding the AI Development Life Cycle - Impacts of AI 1. Risks of AI 2. Benefits of AI
Topic 3: AI Laws, Regulations, and Standards	- Regional and National Regulations 1. EU AI Act 2. National AI Strategies 3. US State and Federal Regulations - Global AI Frameworks 1. UNESCO AI Ethics 2. OECD AI Principles 3. ISO/IEC Standards
Topic 4: 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
Topic 5: AI Risk Management	- Risk Frameworks and Standards 1. ISO 31000 Risk Management 2. NIST AI Risk Management Framework (RMF) - Operationalizing Risk Management 1. Risk identification and assessment 2. Risk mitigation strategies

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q77-Q82):

NEW QUESTION # 77
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step to address this?

A. Run red-teaming exercises.
B. Replace the model with a previous version.
C. Conduct champion/challenger testing.
D. Perform an audit of the model.

Answer: C

Explanation:
Champion/challenger testing allows comparison between the current model and alternatives to identify if a replacement or adjustment is necessary, addressing accuracy deterioration systematically.

NEW QUESTION # 78
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?

A. Upon initial deployment of the system.
B. When the algorithmic impact assessment has been completed.
C. When use of the system causes or is likely to cause material harm.
D. Upon release of a new version of the system.

Answer: A

Explanation:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.

NEW QUESTION # 79
ISO/IEC 22989 and 42001 can be valuable resources for AI Governance professionals in all of the following ways EXCEPT:

A. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV).
B. Establishing terminology and describing concepts so that governance team members can communicate with diverse parties and stakeholders from around the world.
C. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization.
D. Being applicable to organizations of any size and industry seeking to use AI responsibly and effectively in their design processes, information systems and controls.

Answer: C

Explanation:
ISO/IEC 22989 and 42001 provide foundational concepts, terminology, and governance guidance for AI systems, but they do not address the detailed, specific processes required for managing procurement with third-party AI providers.

NEW QUESTION # 80
Which of the following best describes the data minimization principle as it relates to an AI model?

A. Review all the data manually.
B. Include techniques such as adding noise or using synthetic data.
C. Avoid training the model on personal data.
D. Only use personal data during the inference phase.

Answer: B

Explanation:
A best reflects practical application of data minimization to machine learning. Data minimization requires personal data to be adequate, relevant, and limited to what is necessary for the specified purpose. AI development does not categorically prohibit using personal data in training, so B is too absolute. Personal data may legitimately be required during both training and inference, making D incorrect. Manual review of every data item is neither the definition nor a necessary implementation of minimization. Privacy-enhancing techniques can instead reduce the amount or identifiability of personal data processed while retaining model utility. ICO AI guidance specifically identifies perturbation or adding noise, synthetic data, and federated learning as techniques that can support data minimization during model training. Thus, A is the technically and governance-aligned choice.

NEW QUESTION # 81
Which of the following is a foundational characteristic of effective AI governance?

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

Answer: D

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 # 82
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