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Vendor: | IAPP |
---|---|
Exam Code: | AIGP |
Exam Name: | Artificial Intelligence Governance Professional |
Exam Questions: | 164 |
Last Updated: | October 6, 2025 |
Related Certifications: | IAPP Certification Programs |
Exam Tags: | Professional AI project managersAI governance professionals |
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Retrieval-Augmented Generation (RAG) is defined as?
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline---allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high-stakes environmentswhere hallucinations are a concern.
B, C, and Ddescribe optimization or bias mitigation---not the core function of RAG.
Which risk management framework/guide/standard focuses on value-based engineering methodology?
The IEEE 7000-2021 Standard focuses on a value-based engineering methodology for addressingethical concerns during system design. This standard guides engineers and organizations in integrating ethical considerations into the design and development processes of AI systems, ensuring that these technologies are developed responsibly and align with human values. Reference: AIGP Study Material, section on risk management frameworks and standards.
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.
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?
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 Report 2024 also 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.
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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?
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 AImodels.
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