AIGP LATEST EXAM ANSWERS - VALID DUMPS AIGP SHEET

AIGP Latest Exam Answers - Valid Dumps AIGP Sheet

AIGP Latest Exam Answers - Valid Dumps AIGP Sheet

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Valid Dumps AIGP Sheet | Latest AIGP Exam Question

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Topic 2
  • Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
Topic 3
  • 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 4
  • Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q68-Q73):

NEW QUESTION # 68
A UK company has designed a facial recognition model to support border control.
The EU AI Act would apply to the model in all of the following situations EXCEPT if?

  • A. The model was trained by an EU company.
  • B. The model is deployed at an EU border checkpoint.
  • C. The model was released under an open source license.
  • D. The model is deployed at UK border checkpoints.

Answer: D

Explanation:
TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systems impact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or used entirely outside of the EU-such as in theUK, unless they affect the EU market or individuals.
From theAI Governance in Practice Report 2024:
"The act imposes regulatory obligations... depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems." (p. 7)
"The EU AI Act is the world's first comprehensive AI regulation... requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market." (p. 7-8) Thus:
* A. Open source releasedoes not exclude applicability if deployed in the EU.
* B. Deployment at an EU borderclearly invokes jurisdiction.
* D. Training by an EU companycreates jurisdictional links.
* C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.


NEW QUESTION # 69
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?

  • A. Testing should minimize human involvement to the extent practicable.
  • B. Testing should be performed specific to the intended uses.
  • C. The test data should be representative of the expected operationaldata.
  • D. The test data should be anonymized to the extent practicable.

Answer: A

Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.


NEW QUESTION # 70
What is the most important reason for documenting risks when developing an AI system?

  • A. To provide transparency to stakeholders.
  • B. To mitigate potential liability.
  • C. To promote knowledge sharing.
  • D. To align with industry standards.

Answer: B

Explanation:
The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.
From theAI Governance in Practice Report 2024:
"An effective AI governance model is about collective responsibility... which should encompass oversight mechanisms such as privacy, accountability, compliance." (p. 13)
"Accountability... is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes." (p. 28) While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation's primary role is liability mitigation.


NEW QUESTION # 71
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

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

Answer: A

Explanation:
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).


NEW QUESTION # 72
According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?

  • A. Is necessary for entering into or performing under a contract between the data subject and data controller.
  • B. Is authorized with the data subject s explicit consent.
  • C. Is deemed to solely benefit the individual and includes documented legitimate interests.
  • D. Is authorized by applicable Ell law and includes suitable safeguards.

Answer: B

Explanation:
According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject's explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.


NEW QUESTION # 73
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