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[General] New AIGP Study Guide, AIGP Latest Dumps Ppt

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【General】 New AIGP Study Guide, AIGP Latest Dumps Ppt

Posted at before yesterday 06:23      View：8 | Replies：1        Print      Only Author   [Copy Link] 1#
P.S. Free 2026 IAPP AIGP dumps are available on Google Drive shared by TrainingQuiz: https://drive.google.com/open?id=1X5ojPGR7czjsssuVPKm_Qw68haq1Qoeu
We're committed to ensuring you have access to the best possible AIGP questions. We offer AIGP dumps in PDF, web-based practice tests, and desktop practice test software. We provide these AIGP questions in all three formats since each has useful features of its own. If you prepare with IAPP Certified Artificial Intelligence Governance Professional (AIGP) actual dumps, you will be fully prepared to pass the test on your first attempt.
IAPP AIGP Exam Syllabus Topics:
TopicDetails
Topic 1
  • Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
Topic 2
  • Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.
Topic 3
  • Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Topic 4
  • Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q85-Q90):NEW QUESTION # 85
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
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. Continue to require the company's hiring personnel to manually screen all applicants
  • C. Ensure the vendor provides indemnification for the AI tool
  • D. Require the procurement and deployment teams to agree upon the AI tool
Answer: A
Explanation:
The correct answer isA. Pre- and post-deployment testing ensuresbias, accuracy, and fairnessare evaluated and corrected as needed, which isessential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report2025(Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).

NEW QUESTION # 86
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?
  • A. Explainable and Interpretable.
  • B. Accountable and Transparent.
  • C. Secure and Resilient.
  • 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 # 87
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?
  • A. The data aggregator.
  • B. The tech company.
  • C. The marketing agency's client.
  • D. The marketing agency.
Answer: A
Explanation:
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data's lawful collection by the databroker..." (p.
19)
This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

NEW QUESTION # 88
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?
  • A. Uncertainty.
  • B. Forgetting.
  • C. Hallucination.
  • D. Brittleness.
Answer: D
Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

NEW QUESTION # 89
What is the primary purpose of an Al impact assessment?
  • A. To define and document the roles and responsibilities of Al stakeholders.
  • B. To identify and measure the benefits of an Al system.
  • C. To define and evaluate the legal risks associated with developing an Al system.
  • D. Anticipate and manage the potential risks and harms of an Al system.
Answer: D
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 # 90
......
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Posted at 6 hour before        Only Author  2#
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