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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
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	Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
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. Product liability laws.
B. Privacy laws.
C. Anti-discriminationlaws.
D. Accessibility laws.

Answer: A

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 # 30
All of the following are reasons to deploy a challenger Al model in addition a champion Al model EXCEPT to?

A. Retrain the champion model.
B. Perform testing on the champion model.
C. Provide a framework to consider alternatives to the champion model.
D. Automate real-time monitoring of the champion model.

Answer: A

Explanation:
Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.
Reference: AIGP BODY OF KNOWLEDGE, sections on model evaluation and management.

NEW QUESTION # 31
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?

A. Reliable.
B. Resilient.
C. Reinforced.
D. Robust.

Answer: B

Explanation:
An AI system that maintains its level of performance within defined acceptable limits despite real-world or adversarial conditions is described as resilient. Resilience in AI refers to the system's ability to withstand and recover from unexpected challenges, such as cyber-attacks, hardware failures, or unusual input data. This characteristic ensures that the AI system can continue to function effectively and reliably in various conditions, maintaining performance and integrity. Robustness, on the other hand, focuses on the system's strength against errors, while reliability ensures consistent performance over time. Resilience combines these aspects with the capacity to adapt and recover.

NEW QUESTION # 32
What is the primary purpose of an Al impact assessment?

A. To define and document the roles and responsibilities of Al stakeholders.
B. To define and evaluate the legal risks associated with developing an Al system.
C. Anticipate and manage the potential risks and harms of an Al system.
D. To identify and measure the benefits of an Al system.

Answer: C

Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.

NEW QUESTION # 33
A company initially intended to use a large data set containing personal information to train an Al model.
After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?

A. Federated learning.
B. Pseudonymization.
C. Anonymization.
D. Differential privacy.

Answer: C

Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.

NEW QUESTION # 34
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New AIGP Test Registration - AIGP Reliable Practice Materials

IAPP AIGP Exam Syllabus Topics:

Top New AIGP Test Registration | Efficient IAPP AIGP: IAPP Certified Artificial Intelligence Governance Professional 100% Pass

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q29-Q34):