Pass the IAPP Artificial Intelligence Governance AIGP Questions and answers with Dumpstech

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.

===========

Under the EU AI Act, high-risk AI systems likeCrime Buster9619 would require a detailed conformity assessment before being deployed in the EU market. This assessment ensures that the AI system complies with all relevant regulations and standards, addressing potential risks related to privacy, security, and discrimination. The company would not need to register the tool with the EU database (A), seek approval from an EU authority (B), or face a ban (D) as long as it meets the necessary conformity requirements​​​​.

Strong AI, also known as artificial general intelligence (AGI), refers to AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks, similar to human cognitive abilities. For the self-driving car to be classified as "strong" AI, it would need to possess full human cognitive abilities to make independent decisions beyond pre-programmed instructions. Reference: AIGP BODY OF KNOWLEDGE and AI classifications.

The correct answer isA. TheARIA programby NIST is explicitly designed tosupport stakeholders in understanding and managing the risks and impactsof AI systems.

From the AIGP ILT Guide – U.S. Risk Frameworks Module:

“NIST’s ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use.”

Also cited in the AI Governance in Practice Report2025(Frameworks Section):

“ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications.”

ARIA isnota red-teaming or sandbox program—it’s anassessment and governance resource.

===========

The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

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.

Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

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.

AI governance frameworks consistently define the“unique characteristics”of AI that create novel governance challenges.

Across the OECD AI Principles, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, the key characteristics requiring governance are:

Autonomy– AI systems act without direct human intervention and take context-dependent decisions.

Adaptability– AI systems learn, evolve, and update their behavior over time, making outputs non-deterministic.

These traits fundamentally distinguish AI from traditional software and shape all major governance activities (risk assessment, monitoring, assurance, accountability, alignment, etc.).

Below is why each option is correct or incorrect:

A. Autonomy — NOT selected

Reason:

Autonomy is repeatedly cited as one of the defining characteristics that necessitates governance.

NIST AI RMF references the risks ofautonomous behavior.

ISO/IEC 42001 emphasizes governance controls for “systems operating with varying levels of autonomy.”

Thus,Autonomy IS a unique characteristic, so it isnotan answer.

B. Automation — SELECTED

Reason:

“Automation” is not a unique AI property. Automation predates AI and applies to robotics, scripts, business process automation, etc.

AI governance literature doesnotclassify automation as a unique AI-specific characteristic.

Automation is aresultof AI, but not adistinguishing property.

Therefore,Automation is correctly selected as NOT a unique AI characteristic.

C. Adaptability — NOT selected

Reason:

Adaptability (systems that update, learn, or change behavior) is recognized universally as a core distinctive trait of AI.

EU AI Act emphasizes governance requirements for “adaptive systems.”

NIST AI RMF highlights “learning and emergent behavior.”

Thus,Adaptability IS a unique characteristic, so it shouldnotbe chosen as an exception.

D. Speed and scale — SELECTED

Reason:

While AI can operate at high speed and scale,these are not unique characteristics of AI.

Traditional computing systems, cloud workloads, and automation pipelines operate at similar scale.

AI governance documents mention speed/scale as arisk amplifier, not a defining characteristic.

Therefore,Speed and scaleshould be selected.

E. Superintelligence — SELECTED

Reason:

“Superintelligence” is not a characteristic of current AI systems and isnot referenced as a governance-relevant attributein mainstream policy documents (OECD, NIST, ISO, EU AI Act).

It is speculative rather than a defining feature.

Therefore, it is correctly chosen as NOT a unique characteristic requiring governance today.