AIGP Exam Dumps - Artificial Intelligence Governance Professional

The correct answer isA. While TEVV is important inlater lifecycle phases, it isnot the primary considerationwhen evaluating and prioritizinguse cases.

From the AIGP Body of Knowledge – Use Case Assessment Module:

“Use case evaluation focuses on value, impact, fairness, and accessibility—technical testing considerations come later.”

ILT Guide confirms:

“Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization.”

Thus,Ais not a top-level consideration during use caseselection.

The correct answer isA. Only GPAI modelswith systemic riskmustpublish a detailed summary of training datato meet transparency and accountability standards under the EU AI Act.

From the AI Governance in Practice Report2025(EU AI Act Section):

“For GPAI systems with systemic risk, providers must publish sufficiently detailed summaries of the content used to train the model.”

Also, the AIGP ILT Guide confirms:

“The obligation to disclose summaries of training data applies only to systemic-risk GPAI models, not all general-purpose models or high-risk systems.”

This unique requirement is part of the Act’s effort to increasetransparency and auditabilityfor powerful foundational models.

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Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.

The correct answer isD. AI Impact Assessments are primarily used to identify and managerisks and harmsassociated with AI systems.

From the AIGP Body of Knowledge:

“The goal of an AI impact assessment is to ensure that risks are identified, evaluated, and mitigated prior to or during development and deployment.”

As further confirmed in the AI Governance in Practice Report2025(Part III):

“Risk-based tools like DPIAs and Algorithmic Impact Assessments help identify potential risks to individuals and society, enabling organizations to implement mitigation plans and safeguards.”

While benefits may be noted in such assessments, thecore objectiveis to manage risks andpromote responsible AI.

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The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.