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NEW QUESTION # 90
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
During the first month when the company monitors the model for bias, it is most important to:
Answer: B
Explanation:
During initial production monitoring, the most important action is to continue disparity testing to detect and quantify whether the model is producing biased outcomes across protected groups.
This directly aligns with the goal of evaluating fairness, accuracy, and reliability, enabling the organization to identify and respond to discriminatory patterns early.
NEW QUESTION # 91
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: D
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 # 92
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?
Answer: B
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 # 93
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 agency has taken governance actions such as:
* Conducting an impact assessment
* Providing legal disclosures
* Enabling bias mitigation and explainability
* Complying with regulatory requirements
Which of the following should be included in the marketing company's disclosures about the use of the LLM EXCEPT?
Answer: A
Explanation:
The correct answer is B - Proprietary methods. While transparency is important, organizations are not obligated to disclose proprietary algorithms, methods, or trade secrets in public disclosures.
From the AIGP Body of Knowledge - Transparency & Disclosures:
"AI system users should disclose the purpose, capabilities, limitations, and applicable legal context-but not sensitive IP." AI Governance in Practice Report 2024 (Transparency Section) states:
"Disclosure requirements balance public understanding with the need to protect proprietary business interests.
Proprietary training methods are not expected to be disclosed."
Thus, while it's best practice to disclose the intended purpose, legal compliance, and system limitations, internal proprietary techniques are usually excluded.
NEW QUESTION # 94
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?
Answer: B
Explanation:
The correct answer is B because continuous monitoring is a core component of AI governance that ensures systems remain effective, reliable, and aligned with their intended objectives over time. AI systems can degrade due to data drift, model drift, or changing real-world conditions, making ongoing performance tracking essential. Monitoring allows organizations to detect anomalies, biases, or performance issues and take corrective actions such as retraining or recalibration. Governance frameworks emphasize post- deployment oversight to ensure systems continue to operate safely and within acceptable risk thresholds.
Option A incorrectly suggests fully autonomous evolution without oversight, which contradicts governance principles. Options C and D address specific operational concerns but do not capture the primary purpose of continuous monitoring, which is maintaining performance, accountability, and alignment with defined goals.
NEW QUESTION # 95
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