Free IAPP AIGP Practice Exam with Questions & Answers

Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.

The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.

The correct answer is D – Privacy impact assessments (PIAs). These are directly adaptable for identifying risks in AI systems, particularly around data usage, bias, and individual impacts.

From the AIGP ILT Guide – Risk Management Module:

“PIAs and DPIAs are existing tools used in privacy compliance that can be extended to evaluate the risks of AI, including fairness, explainability, and legality.”

AI Governance in Practice Report 2024 further explains:

“Organizations can adapt privacy impact assessments to evaluate the ethical, legal, and technical risks posed by AI systems. They provide a structured and recognized method.”

PIAs are preferable over general security practices (like pen testing) which do not address algorithmic bias or legal compliance directly.

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In selecting the specific type of algorithm for the AI solution, the healthcare network's data science team is most important. This team possesses the technical expertise and understanding of the data, the clinical context, and the performance requirements needed to make an informed decision about which algorithm is most suitable. While the cloud provider and consulting firm can offer support and infrastructure, and the AI governance committee provides oversight, the data science team’s specialized knowledge is crucial for selecting and implementing the appropriate algorithm. Reference: AIGP Body of Knowledge, AI governance and team roles section.

The correct answer is C. ISO/IEC 22989 and 42001 focus on terminology, risk, and management systems, but do 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 2024:

“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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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'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.

The correct answer is C. This action ties directly into principles of data minimization, purpose limitation, and lawfulness of processing, which are central to privacy and AI governance.

From the AIGP Body of Knowledge, Section on Privacy Considerations:

“Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA.”

Additionally, AI Governance in Practice Report 2024 emphasizes:

“One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose… Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk.”

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The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks​​​​.

The correct answer is B – The tech company. The party that develops and trains the foundational model is responsible for ensuring the lawful collection of training data.

From the AIGP ILT Guide – Foundational Models & Data Governance:

“Responsibility for the lawfulness of data collection typically lies with the party that trains the model—usually the provider or developer of the foundational model.”

AI Governance in Practice Report 2024 confirms:

“General Purpose AI providers are required to ensure that training data is lawfully acquired, including compliance with intellectual property and privacy requirements.”

The marketing agency is only a user or downstream integrator, not responsible for original data collection.