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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk Frameworks and Standards
|
| Topic 2: AI Governance Implementation | - Organizational Governance
|
| Topic 3: AI Laws, Regulations, and Standards | - Global AI Frameworks
|
| Topic 4: AI Governance Management | - Emerging Issues
|
| Topic 5: Foundations of AI Governance | - AI Governance Fundamentals
|
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NEW QUESTION # 19
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:
Answer: D
Explanation:
TEVV (testing, evaluation, verification, and validation) metrics apply after a use case is selected and designed. They are not a primary factor when initially evaluating which AI use cases to pursue.
NEW QUESTION # 20
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The technology company has also addressed environmental concerns and societal harms.
Which of the following results would be considered biased outputs from this AI system EXCEPT?
Answer: A
Explanation:
The correct answer isA. Sending ads to construction companies (business entities) rather than individual workers isa business targeting decision, not inherently a biased AI output.
From the AIGP ILT Participant Guide - Bias & Fairness Module:
"Biased outputs often include stereotyping, exclusion of underrepresented groups, or reinforcing harmful societal assumptions." Examples likeinsufficient representation of minority groupsorgender-stereotyping in visuals or languageare typical manifestations of bias.
AI Governance in Practice Report2025also notes:
"Bias in generative models may manifest in representation gaps, stereotyping, or unequal performance across demographic groups." Option A, by contrast, describes adistribution strategy, not a bias generated by the AI model.
NEW QUESTION # 21
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The AI system works by surveying the public sites in order to identify individuals that are likely to have committed a crime.
It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant to assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its AI system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the AI system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the AI system gives a score of at least 90% and proceed directly with an arrest.
Which AI risk would NOT have been identified during the procurement process based on the categories of information requested by the third-party consultant?
Answer: C
Explanation:
The consultant evaluated accuracy, training data diversity (related to discrimination), and system workings (explainability), but security risks were not assessed during procurement.
NEW QUESTION # 22
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a large organization least likely to use to responsibly train stakeholders on AI terminology, strategy and governance?
Answer: A
Explanation:
The correct answer isA. While educating technical staff is important, expectingall technical employees to be retooled as AI developersis unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should berole-specificand align with the individual's function and responsibilities... Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report 2025 supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus,broad development training for all technical employeesis the least practical and least likely approach.
NEW QUESTION # 23
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?
Answer: B
Explanation:
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2024:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34)
* Ais about liability, not auditability.
* Bmatters for IP rights, not oversight.
* Crelates to lifecycle responsibility but doesn't guarantee audit access.
NEW QUESTION # 24
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