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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 2	Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
Topic 3	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.
Topic 4	Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 5	Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
Topic 6	Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q131-Q136):

NEW QUESTION # 131
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 release of a new version 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 initial deployment of the system.

Answer: D

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 # 132
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

A. Department of Homeland Security (DHS).
B. National Science and Technology Council (NSTC).
C. National Institute of Standards and Technology (NIST).
D. Office of Science and Technology Policy (OSTP).

Answer: C

Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.

NEW QUESTION # 133
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Secure and Resilient.
B. Tested and Effective.
C. Explainable and Interpretable.
D. Accountable and Transparent.

Answer: B

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.

NEW QUESTION # 134
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?

A. Statistical sampling.
B. Risk probability/severity.
C. Decision analysis.
D. Adversarial robustness.

Answer: B

Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.

NEW QUESTION # 135
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?

A. Al systems trained on sensitive personal data.
B. Al systems that are high-risk.
C. Al systems that are harmful based on a legal risk-utility calculation.
D. Al systems that are "strong" general intelligence.

Answer: B

Explanation:
According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards.
High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.

NEW QUESTION # 136
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