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IAPP AIGP Prüfungsplan:

Thema	Einzelheiten
Thema 1	Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
Thema 2	Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
Thema 3	Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Thema 4	Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q216-Q221):

216. Frage
Please select 3 of the 5 options below. No partial credit will be given.
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT?

A. Superintelligence.
B. Autonomy.
C. Automation.
D. Speed and scale.
E. Adaptability.

Antwort: A,C,D

Begründung:
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.

217. Frage
Retraining an LLM can be necessary for all of the following reasons EXCEPT:

A. To minimize degradation in prediction accuracy due to changes in data.
B. To ensure interpretability of the model's predictions.
C. To adjust the model's hyperparameters to a specific use case.
D. To account for new interpretations of the same data.

Antwort: B

Begründung:
Retraining focuses on adapting the model to data changes or specific use cases, but it does not directly ensure interpretability of predictions, which involves different methods.

218. Frage
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Perform a readiness assessment.
B. Identify known edge cases to monitor post-deployment.
C. Document maintenance teams and processes.
D. Define a model-validation methodology.

Antwort: A

Begründung:
Performing a readiness assessment ensures the model and supporting systems are fully prepared for deployment, addressing risks and compliance before going live.

219. Frage
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?

A. Organizational accountability.
B. Explainability of results.
C. Internal governance needs.
D. Diversity of data sources.

Antwort: C

Begründung:
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.

220. Frage
What is most likely the first action that a developer takes to map, plan and scope an AI project?

A. Determine feasibility and optionality of redress
B. Use a test, evaluation, verification, validation TEVV process
C. Perform an algorithmic impact assessment leveraging PIAs
D. Define the business case and perform a cost benefit analysis answering the question of why AI

Antwort: D

Begründung:
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 considerations arise later as part of risk management and governance. Starting with a clear "why AI" foundation ensures responsible, efficient, and goal-oriented system development.

221. Frage
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