AIGP Exam Dumps - IAPP Artificial Intelligence Governance Questions and Answers

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model ' s predictions. Model drift is when the model ' s performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

External communication plans primarily establish how an organization communicates relevant AI information, risks, incidents, limitations, deployment changes, and governance matters to people outside the organization. A therefore provides the broadest and most accurate purpose. The current IAPP AIGP Study Guide expressly treats external communications as part of ongoing AI operations and associates them with transparency and trust-building. NIST AI RMF likewise requires communication plans for identified and measured AI risks and encourages communication with external stakeholders, affected communities, users, and other relevant AI actors. B concerns internal training rather than external communication. C belongs to third-party procurement and contract governance. Regulatory notification in D may form one component of an external communication plan when legally required, but it is considerably narrower than the overall governance objective of transparent stakeholder communication represented by A.

The NIST AI Risk Management Framework’s Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross-functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization’s AI principles and ethical standards. Reference: NIST AI Risk Management Framework documentation, Govern function section.

The TAKE IT DOWN Act, Public Law 119-12, criminalizes certain intentional disclosures of authentic and AI-generated nonconsensual intimate imagery. Offenses involving minors can carry imprisonment of up to three years, supporting A. Covered platforms must establish a notice-and-removal process and receive liability protection when they disable or remove reported material in good faith, supporting E. Violations of the platform requirements are enforceable by the FTC and can trigger substantial civil penalties, supporting D conceptually. The video ' s approximately $51,000 figure reflects the civil-penalty level used in supporting governance materials; current FTC guidance lists a higher inflation-adjusted maximum of $53,088 per violation. B is clearly incorrect because the statutory removal deadline is 48 hours , not 30 days. C is not itself a specified statutory penalty.

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A best reflects practical application of data minimization to machine learning. Data minimization requires personal data to be adequate, relevant, and limited to what is necessary for the specified purpose. AI development does not categorically prohibit using personal data in training, so B is too absolute. Personal data may legitimately be required during both training and inference, making D incorrect. Manual review of every data item is neither the definition nor a necessary implementation of minimization. Privacy-enhancing techniques can instead reduce the amount or identifiability of personal data processed while retaining model utility. ICO AI guidance specifically identifies perturbation or adding noise, synthetic data, and federated learning as techniques that can support data minimization during model training. Thus, A is the technically and governance-aligned choice.

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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 2025 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 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.

The correct answer is D because AI impact assessments primarily focus on risks to individuals, society, and legal compliance, such as privacy, fairness, and potential harm. Core elements typically include evaluating the impact on privacy rights, identifying data sources and their lawfulness, and assessing potential bias or discriminatory outcomes. These factors are directly tied to regulatory expectations and ethical AI governance principles. While environmental considerations like energy consumption are increasingly discussed in broader AI sustainability conversations, they are not standard or required components of most AI impact assessments, especially for narrow use cases. AI governance frameworks prioritize human-centric risks, ensuring systems are lawful, fair, and safe, rather than focusing on operational metrics like energy usage in typical assessment processes.

The correct answer is A because developing a social media presence typically does not require AI and can be effectively achieved through traditional digital marketing strategies, content planning, and manual engagement. AI governance principles emphasize that organizations should first determine whether AI is necessary or proportionate to the problem being solved. Using AI where simpler, deterministic, or human-driven solutions suffice can introduce unnecessary complexity, cost, and risk. In contrast, options B, C, and D involve tasks such as campaign optimization, personalization, and automation, which benefit significantly from AI capabilities like pattern recognition, predictive analytics, and natural language processing. Responsible AI governance encourages a “fit-for-purpose” approach, ensuring AI is only deployed when it provides clear added value over non-AI alternatives.

Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.