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.

AI ' s resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.

Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.

The correct answer isC. ISO/IEC 22989 and 42001 focus onterminology, risk, and management systems, butdo not specifically address procurement-related concerns with third-party vendors.

From the AIGP Body of Knowledge – Standards Section:

“ISO/IEC 22989 defines terminology and foundational concepts. ISO/IEC 42001 provides a management system standard for AI. They are not procurement-focused documents.”

Also confirmed in the AI Governance in Practice Report 2025:

“These standards help establish common language and risk governance procedures. Procurement governance typically falls under separate frameworks or sector-specific guidance.”

Thus,procurement governance (Option C)is not a central use case for these standards.

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The correct answer is C. This option concerns consent, which is generally a privacy, transparency, or institutional governance issue rather than a copyright issue. Copyright risk in AI governance usually relates to whether protected third party works were used in model training, whether generated outputs may reproduce copyrighted expression, and whether content is created without proper attribution or clear ownership boundaries. That makes options A, B, and D directly relevant to copyright and intellectual property concerns. In contrast, whether students must expressly consent deals with notice, fairness, and lawful or ethical use in an educational environment. For AIGP exam purposes, it is important to separate intellectual property risks from privacy and data protection obligations, even when both appear in the same classroom or organizational AI use case.

Feature selection is the most important element of feature engineering to mitigate potential bias in an AI system. This process involves choosing the most relevant and representative features from the data set, which directly affects the model’s performance and fairness. By carefully selecting features, data scientists can reduce the influence of biased or irrelevant attributes, ensuring that the AI system is more accurate and equitable. Proper feature selection helps in eliminating biases that might stem from socio-demographic factors or other sensitive variables, leading to a more balanced and fair AI model. Reference: AIGP Body of Knowledge on Fairness in AI and Feature Engineering.

AI models are intrinsically dependent on the data used to train, validate, and test them. NIST specifically identifies AI ' s inherent dependence on training data and links model reliability to data quality and representativeness. Therefore, A is correct. Training-data characteristics—including modality, structure, features, quantity, and complexity—also influence the appropriate model design and architecture, supporting D. E is correct because biases, gaps, nonrepresentative samples, and other limitations in training data can become embedded in model behavior and subsequently affect outputs. Privacy-enhancing technologies may reduce privacy risks, but they do not define data dependency, eliminating B. Likewise, improving dataset size and quality can improve performance but does not remove the model ' s fundamental dependence on data, so C is incorrect.

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The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company’s competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.

According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.

When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.