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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q79-Q84):

NEW QUESTION # 79
What is the best reason for a company adopt a policy that prohibits the use of generative Al?

A. Avoid the time necessary to train employees on acceptable use.
B. Avoid needing to identify and hire qualified resources.
C. Avoid using technology that cannot be monetized.
D. Avoid accidental disclosure to its confidential and proprietary information.

Answer: D

Explanation:
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company's competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.

NEW QUESTION # 80
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

A. Department of Homeland Security (DHS).
B. National Science and Technology Council (NSTC).
C. National Institute of Standards and Technology (NIST).
D. Office of Science and Technology Policy (OSTP).

Answer: C

Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems.
NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.

NEW QUESTION # 81
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

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

Answer: C

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 # 82
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.
B. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
C. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
D. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.

Answer: A

Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals.
Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

NEW QUESTION # 83
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. How frequently it will be updated.
B. Whether its system card identifies risks.
C. What business purpose it will serve.
D. Whether its training data is disclosed.

Answer: C

Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

NEW QUESTION # 84
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