AIGP Exam Dumps - Artificial Intelligence Governance Professional

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 2025:

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

The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

The correct answer is C because performing an impact assessment is the most effective proactive measure to identify and mitigate discrimination risks before model training and testing. AI governance frameworks emphasize early-stage risk identification, particularly for high-stakes domains like healthcare, where bias can lead to harmful or inequitable outcomes. Impact assessments, such as algorithmic or data protection impact assessments, evaluate potential harms, affected populations, and fairness risks prior to development. This enables organizations to design appropriate safeguards, adjust data selection, and implement mitigation strategies before biases become embedded in the model. Options like audits and bias bounty programs are reactive or post-development controls, while simply acquiring more data does not guarantee reduced bias without proper analysis. A structured impact assessment aligns with risk-based governance and supports fairness, accountability, and regulatory compliance.

C is the strongest answer because proprietary deployment can give an organization greater control over system architecture, permissions, agent behavior, data access, escalation rules, and technical guardrails. This control is especially valuable for agentic AI, where systems may autonomously invoke tools, access organizational data, and execute multistep workflows. IAPP guidance emphasizes that agentic systems require purpose-specific guardrails, including controls limiting the data and actions available to an agent. Proprietary control can therefore enable safeguards to be tailored closely to organizational risk tolerances and business requirements. It does not provide immunity from intellectual-property claims, eliminating A. Organizations cannot simply transfer accountability or all risk to a cloud provider, eliminating B. Greater agent autonomy actually increases the importance of internal governance, monitoring, authorization, and human oversight rather than reducing it, eliminating D.

================

The correct answer isC. Thechoice of architecture(e.g., neural networks vs. decision trees) is typically part of thedesign and developmentphase, not the initial planning.

From the AIGP Body of Knowledge – AI Lifecycle Module:

“Planning involves scoping, context definition, stakeholder identification, governance planning, and assumptions—not yet model selection.”

Confirmed in the ILT Participant Guide:

“Design decisions such as architecture or algorithm type come after planning—usually during development based on technical feasibility and data availability.”

 

 

The correct answer is A because the first step in any AI project lifecycle is to clearly define the business objective and justify the use of AI. AI governance frameworks emphasize that planning begins with understanding the purpose, value, and necessity of the system before moving into design or risk assessment stages. Establishing a business case ensures alignment with organizational goals, identifies expected benefits, and evaluates whether AI is the appropriate solution. This step corresponds to the planning phase of the AI lifecycle, where objectives and intended outcomes are documented. In contrast, TEVV processes occur during development and testing, while impact assessments and redress consid erations arise later as part of risk management and governance. Starting with a clear “why AI” foundation ensures responsible, efficient, and goal-oriented system development.

The potential negative consequences of using an AI tool in hiring include reputational harm (A), civil rights violations (B), and discriminatory treatment (C). These issues stem from biases in the AI system or its misuse, which can lead to unfair hiring practices and legal liabilities. Intellectual property infringement (D) is not a typical consequence of using AI in hiring, as it relates to the unauthorized use of protected intellectual property, which is not directly relevant to the hiring process or the potential biases within AI tools​​​​.

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.