AIGP Dumps with Practice Exam Questions Answers

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.

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.

The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.

Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems — regardless of origin — require evaluation for fairness, accuracy, and representativeness.

From theAI Governance in Practice Report2025:

“Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced.” (p. 41)

An AI system’sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.

From theAI Governance in Practice Report2025:

“There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization’s role, footprint, broader risk-governance profile and maturity.” (p. 4)

“AI governance starts with defining the corporate strategy for AI… and formulating policy standards and operational procedures to reflect industry, use case, and location.” (p. 11)

A– Organizational accountability is broader and not directly scoped by industry or function.

C– Diversity of data sources is tied to data strategy.

D– Explainability is more influenced by model type, not use context.