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Question 191

You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?

Correct Answer: C
In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.
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Question 192

All of the following may be permissible uses of an AI system under the EU AI Act EXCEPT:

Correct Answer: D
The EU AI Act prohibits the use of emotion recognition systems in workplaces because of the high risk to employees' rights and freedoms, making workplace emotion detection an impermissible use.
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Question 193

Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conductandcollective agreementsare what type of assessment tools as defined by theOrganization for Economic Cooperation and Development (OECD)?

Correct Answer: B
The correct answer isB - Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision- making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report 2025 elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.
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Question 194

In 2025, which U.S. agency ordered companies to provide information about the safety of their AI companion chatbots?

Correct Answer: C
The correct answer is C, the Federal Trade Commission. The FTC plays a central role in U.S. AI governance by enforcing consumer protection and unfair or deceptive practices laws. In the context of AI systems such as companion chatbots, the FTC has authority to investigate risks related to user harm, transparency, safety, and misleading claims. AI governance frameworks emphasize regulatory oversight where AI systems may impact individuals psychologically, financially, or socially. Agencies like the FTC focus on ensuring that companies deploying AI systems do not cause harm through unsafe or deceptive practices and that they provide adequate disclosures about risks and system behavior. This aligns with broader AI governance principles of accountability, transparency, and risk management highlighted in the AI Governance in Practice Report 2024 , which stresses the importance of regulatory involvement in managing AI-related risks.
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Question 195

MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Which of the following PETs would be effective in developing AI systems?

Correct Answer: A,B,E
A, B, and E are the intended privacy-preserving choices. Data minimization limits processing to information that is adequate, relevant, and necessary for the defined purpose, reducing privacy exposure during AI development. Anonymization protects individuals by transforming information so that people are no longer identifiable under the applicable standard. Federated learning is a recognized privacy-enhancing technology that enables models to be trained across distributed datasets without centrally sharing the underlying raw training data. The ICO specifically identifies federated learning and related privacy-preserving methods as useful during AI training and explains the relationship between anonymization and data minimization. Data mapping is primarily a governance and inventory activity used to understand data flows, while data cleansing principally improves data quality by correcting or removing inaccurate, incomplete, or unsuitable records; neither is inherently a PET.
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