100% PASS 2025 EFFICIENT IAPP AIGP LATEST TEST SAMPLE

100% Pass 2025 Efficient IAPP AIGP Latest Test Sample

100% Pass 2025 Efficient IAPP AIGP Latest Test Sample

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
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 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 4
  • 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 (Q106-Q111):

NEW QUESTION # 106
During the planning and design phases of the Al development life cycle, bias can be reduced by all of the following EXCEPT?

  • A. Human oversight.
  • B. Feature selection.
  • C. Data collection.
  • D. Stakeholder involvement.

Answer: B

Explanation:
Bias in AI can be reduced during the planning and design phases through stakeholder involvement, human oversight, and careful data collection. While feature selection is critical in the development phase, it does not specifically occur during planning and design. Ensuring diverse stakeholder involvement and human oversight helps identify and mitigate potential biases early, and data collection ensures a representative dataset.
Reference: AIGP Body of Knowledge on AI Development Lifecycle and Bias Mitigation.


NEW QUESTION # 107
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a large organization least likely to use to responsibly train stakeholders on AI terminology, strategy and governance?

  • A. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
  • B. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
  • C. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
  • D. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems

Answer: D

Explanation:
The correct answer is A. While educating technical staff is important, expecting all technical employees to be retooled as AI developers is unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should be role-specific and align with the individual's function and responsibilities...
Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report 2024 supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus, broad development training for all technical employees is the least practical and least likely approach.


NEW QUESTION # 108
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 # 109
After initially deploying a third-party AI model, you learn the developer has released a new version.
As deployer of this third-party model, what should you do?

  • A. Retrain the model.
  • B. Audit the model.
  • C. Communicate necessary updates to your users.
  • D. Seek input from data scientists.

Answer: B

Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements - which calls for aformal audit.
From theAI Governance in Practice Report 2024:
"Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance." (p. 12)
"Post-market monitoring includes reassessing the impact of updated models or retraining." (p. 35)


NEW QUESTION # 110
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al 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 Al system works by surveilling 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 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 Al 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 Al 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 Al system gives a score of at least 90% and proceed directly with an arrest.
When notifying an accused perpetrator, what additional information should a police officer provide about the use of the Al system?

  • A. Information about the composition of the training data of the system.
  • B. Information about how the individual was identified by the Al system.
  • C. Information about how the accused can oppose the charges.
  • D. Information about the accuracy of the Al system.

Answer: B

Explanation:
When notifying an accused perpetrator, the police officer should provide information about how the individual was identified by the AI system. This transparency is crucial for maintaining trust and ensuring that the accused understands the basis of the charges against them. Information about the accuracy, how to oppose the charges, and the composition of the training data, while potentially relevant, do not directly address the immediate need for the accused to understand the specific process that led to their identification. Reference:
AIGP Body of Knowledge on AI Transparency and Explainability.


NEW QUESTION # 111
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