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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
Topic 3	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 4	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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q138-Q143):

NEW QUESTION # 138
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

A. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
B. Autoregression, which analyzes and makes predictions about time-series data.
C. Momentum, which improves the convergence speed and stability of neural network training.
D. Backpropagation, which starts from the last layer working backwards.

Answer: B

Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.
Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.

NEW QUESTION # 139
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

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

Answer: D

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 # 140
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent.
The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
If XYZ does not deploy and use the Al hiring tool responsibly in the United States, its liability would likely increase under all of the following laws EXCEPT?

A. Anti-discriminationlaws.
B. Product liability laws.
C. Privacy laws.
D. Accessibility laws.

Answer: B

Explanation:
In the United States, the use of AI hiring tools must comply with anti-discrimination laws, accessibility laws, and privacy laws to avoid increasing liability. Anti-discrimination laws (A) ensure that hiring practices do not unlawfully discriminate against protected classes. Accessibility laws (C) require that hiring tools are accessible to all applicants, including those with disabilities. Privacy laws (D) govern the handling of personal data during the hiring process. Product liability laws (B), however, typically apply to the safety and reliability of physical products and would not generally increase liability specifically related to the responsible use of AI hiring tools in the employment context.

NEW QUESTION # 141
Which of the following most encourages accountability over Al systems?

A. Understanding Al legal and regulatory requirements.
B. Defining the roles and responsibilities of Al stakeholders.
C. Performing due diligence on third-party Al training and testing data.
D. Determining the business objective and success criteria for the Al project.

Answer: B

Explanation:
Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.

NEW QUESTION # 142
Scenario:
A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act.
The regulator is considering penalties under the enforcement provisions of the regulation.
According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to €
15 million or 3% of annual worldwide turnover (whichever is higher)?

A. In case of AI Act prohibitions
B. In case of breach of a provider's obligations for high-risk AI systems
C. In case of a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies
D. In case of the supply of misleading information to notified bodies in reply to a request

Answer: B

Explanation:
The correct answer is B. The EU AI Act assigns a tiered penalty system based on the severity of the violation. A breach of obligations related to high-risk AI systems falls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.
From the AIGP ILT Guide - EU AI Act Module:
"Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover." AI Governance in Practice Report 2024 supports this:
"Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article
71(3) of the EU AI Act."
Note: The highest penalty (€35 million or 7%) applies to prohibited AI uses, not to obligations for high- risk systems.

NEW QUESTION # 143
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