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| Section | Weight | Objectives |
|---|---|---|
| Governance of AI Development | 21–25% | - Requirements gathering, risk assessment and mitigation - Governance through design and lifecycle management - Testing, validation, documentation and transparency - Model management, version control and quality assurance |
| Laws, Regulations, Standards and Frameworks | 24–28% | - International standards (ISO 42001, NIST AI RMF, etc.) - Alignment with data protection and privacy laws - Global and regional regulations (EU AI Act, US, etc.) - Sector-specific requirements and compliance obligations |
| Governance of AI Deployment and Use | 21–25% | - Incident response, accountability and audit - Selection, procurement and third-party management - Lifecycle maintenance, decommissioning and updates - Implementation, monitoring and ongoing oversight |
| Foundational Concepts of AI Governance | 24–28% | - Fundamental AI definitions, types and characteristics - Principles of responsible, ethical and trustworthy AI - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts |
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NEW QUESTION # 148
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: C
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 # 149
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?
Answer: D
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 # 150
What is most likely the first action that a developer takes to map, plan and scope an AI project?
Answer: A
Explanation:
The first action is to clearly define the business case and conduct a cost/benefit analysis to justify the use of AI and set project scope.
NEW QUESTION # 151
Within an established AI governance infrastructure, what might be the most effective governance action to handle third-party AI systems deemed to be high-risk?
Answer: A
Explanation:
Aligning impact assessment activities with regulatory and legal requirements ensures that high- risk third-party AI systems are evaluated rigorously and managed in compliance with relevant standards.
NEW QUESTION # 152
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:
Answer: A
Explanation:
The EU AI Act applies to AI systems used within the EU or by entities established in the EU.
Deployment of the model solely at UK border checkpoints falls outside the Act's jurisdiction since the UK is no longer an EU member.
NEW QUESTION # 153
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