AIGP Übungsmaterialien & AIGP Lernführung: IAPP Certified Artificial Intelligence Governance Professional & AIGP Lernguide

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IAPP AIGP Exam Syllabus Topics:

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

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q182-Q187):

182. Frage
CASE STUDY
Please use the following to answer the next question:
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.
To address these concerns, the company is considering using a third-party AI tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party AI-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 deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by AI hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the AI hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?

A. Test the AI tool pre- and post-deployment.
B. Ensure the vendor provides indemnification for the AI tool.
C. Require the procurement and deployment teams to agree upon the AI tool.
D. Continue to require the company's hiring personnel to manually screen all applicants.

Antwort: A

Begründung:
Testing the AI tool before and after deployment helps identify biases and errors early, reducing the risk of reputational harm by ensuring the system performs fairly and reliably.

183. Frage
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

A. To enable and encourage participation by external stakeholders.
B. To determine responsibility for allocating budgetary resources.
C. To establish diverse, equitable and inclusive processes.
D. To empower and train appropriate cross-functional teams.

Antwort: D

Begründung:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross-functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference: NIST AI Risk Management Framework documentation, Govern function section.

184. Frage
Business A sells software that provides users with writing and grammar assistance.
Business B is a cloud services provider that trains its own AI models.
- Business A has decided to add generative AI features to their
software.
- Rather than create their own generative AI model, Business A has
chosen to license a model from Business B.
- Business A will then integrate the model into their writing
assistance software to provide generative AI capabilities.
- Business A is most concerned that its writing assistance software
could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?

A. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources.
B. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B.
C. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate.
D. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text.

Antwort: D

Begründung:
Business A must test the licensed model to ensure it meets their specific safety requirements, including filtering toxic or obscene content. This directly validates whether the integrated model performs safely within their product before releasing it to users.

185. Frage
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

A. Segmentation.
B. Expert systems.
C. Generative Al.
D. Supervised learning.

Antwort: D

Begründung:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).

186. Frage
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The AI system works by surveying the public sites in order to identify individuals that are likely to have committed a crime.
It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant to assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its AI system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the AI system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the AI system gives a score of at least 90% and proceed directly with an arrest.
What is the best reason the police department should continue to perform investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90%?

A. Because AI systems that affect fundamental civil rights should not be fully automated.
B. Because investigations may identify additional individuals involved in the crime.
C. Because investigations may uncover information relevant to sentencing.
D. Because the department did not perform an impact assessment for this intended use.

Antwort: A

Begründung:
AI systems impacting fundamental civil rights, such as arrests, require human oversight to prevent unfair automated decisions and ensure due process.

187. Frage
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