VALID AIGP TEST REVIEW - AIGP NEW APP SIMULATIONS

Valid AIGP Test Review - AIGP New APP Simulations

Valid AIGP Test Review - AIGP New APP Simulations

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Tags: Valid AIGP Test Review, AIGP New APP Simulations, AIGP Exam Reviews, AIGP High Passing Score, New AIGP Test Question

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Exact Inside Valid AIGP Test Review Questions and Answers

Our web-based practice exam software is an online version of the IAPP AIGP practice test. It is also quite useful for instances when you have internet access and spare time for study. To study and pass the IAPP AIGP Exam on the first attempt, our web-based IAPP AIGP practice test software is your best option. You will go through IAPP Certified Artificial Intelligence Governance Professional mock exams and will see for yourself the difference in your preparation.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which of the following is an obligation of an importer of high-risk AI systems under the EU AI Act?

  • A. Affix the CE marking.
  • B. Verify the Declaration of Conformity.
  • C. Provide technical documentation.
  • D. Conduct a data protection impact assessment.

Answer: B

Explanation:
Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenot the parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report 2024:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act for importers.


NEW QUESTION # 83
During the first month when the company monitors the model for bias, it is most important to?

  • A. Continue disparity testing.
  • B. Provide regular awareness training.
  • C. Document the results of final decisions made by the human underwriter.
  • D. Analyze the quality of the training and testing data.

Answer: A

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking for bias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2024:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41)
* B. Awareness trainingis helpful, but not a technical bias mitigation activity.
* C. Analyzing training datais apre-deploymenttask.
* D. Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.


NEW QUESTION # 84
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
All of the following may be copyright risks from teachers using generative Al to create course content EXCEPT?

  • A. Generative Al is generally trained using intellectual property owned by third parties.
  • B. Students must expressly consent to this use of generative Al.
  • C. Generative Al often creates content without attribution.
  • D. Content created by an LLM may be protectable under U.S. intellectual property law.

Answer: B

Explanation:
All of the options listed may pose copyright risks when teachers use generative AI to create course content, except for students must expressly consent to this use of generative AI. While obtaining student consent is essential for ethical and privacy reasons, it does not directly relate to copyright risks associated with the creation and use of AI-generated content.
Reference: The AIGP Body of Knowledge discusses the importance of addressing intellectual property (IP) risks when using AI-generated content. Copyright risks are typically associated with the use of third-party data and the lack of attribution, rather than the consent of users.


NEW QUESTION # 85
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 empower and train appropriate cross-functional teams.
  • C. To determine responsibility for allocating budgetary resources.
  • D. To establish diverse, equitable and inclusive processes.

Answer: B

Explanation:
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.


NEW QUESTION # 86
Which of the following is a foundational characteristic of effective AI governance?

  • A. Engagement of a cross-functional team
  • B. Thorough reviews of a company's public filings with experts
  • C. Uniform policies and procedures across developer, deployer and user roles
  • D. Reliance on tested vendor management processes

Answer: A

Explanation:
The correct answer is Engagement of a cross-functional team. Effective AI governance requires collaboration among various organizational functions including legal, compliance, IT, ethics, and data science.
From the AIGP Body of Knowledge:
"AI governance cannot be siloed-it requires input and oversight from across departments... A cross- functional team ensures that ethical, technical, legal, and operational risks are all appropriately managed." Also confirmed in the ILT Participant Guide:
"Cross-functional teams allow organizations to bring in different perspectives... Legal, compliance, and technical experts must work together to ensure responsible AI outcomes."


NEW QUESTION # 87
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