Free Practice Questions for IAPP AIGP Exam

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.

Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.

From theAI Governance in Practice Report 2024:

“Most AI requires sizeable amounts of high-quality data… to ensure desired and accurate output.” (p. 15)

“The accuracy of AI model outputs depends significantly on the quality of their inputs.” (p. 24)

“Complex AI systems… with many parameters… result in long development and training phases.” (p. 32)

B. Maturity of governanceaffects oversight, not training time.

D. Number of stakeholdersaffects alignment, not direct training duration.

Under theEU AI Act, serious incidents involvinghigh-risk AI systemsmust be reported. The deployer is required topromptly inform the provider and relevant authoritiesabout the issue.

From theAI Governance in Practice Report 2024:

“Serious incidents involving high-risk systems… must be reported to the provider and relevant market surveillance authority.” (p. 35)

“Timely reporting is required when AI systems result in or may result in violations of fundamental rights.” (p. 35)

Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.

To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.

Offeringloan products based on current offerings and rulesrequires a system that can followexplicit business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that uses “if-then” logic, is ideal here.

From the AI governance context:

“Rule-based AI systems are often preferred when decisions must adhere to precise regulatory or financial criteria.” (aligned with AI best practices in regulated sectors)

A. RAGis used to integrate external knowledge—not suitable for structured, rule-based logic.

B. Multimodal modelshandle varied input types—not needed here.

D. Quantum computingis not yet practical or relevant for this business use case.

The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.

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.

While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remaininternal or shared with regulators.

From theAI Governance in Practice Report 2024:

“Organizations must assess secondary risks… but disclosure is subject to context, regulatory requirements, and risk management discretion.” (p. 30)

A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.