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IAPP AIGP Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 2	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.
Thema 3	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 4	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.

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AIGP Prüfungsguide: IAPP Certified Artificial Intelligence Governance Professional & AIGP echter Test & AIGP sicherlich-zu-bestehen

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q99-Q104):

99. Frage
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has:
- Defined its AI ethical principles.
- Conducted discovery to identify the intended uses and success
criteria for the system.
- Established an AI risk committee.
- Assembled a cross-functional team with clear roles and
responsibilities.
- Created policies and procedures to document standards, workflows,
timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?

A. Ensure the data is labeled and formatted.
B. Identify fits and gasp in the combined data.
C. Evaluate the country of origin of the data.
D. Apply privacy-enhancing technologies to the data.

Antwort: B

Begründung:
Identifying fits and gaps in the combined data helps the healthcare network understand the compatibility and completeness of datasets for effective AI model development.

100. Frage
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The marketing company and its tech provider have taken reasonable steps to govern the AI's use, including legal disclosures, impact assessments, and bias mitigation. However, the company wants to take one more step to improve governance and reduce risks related to ongoing oversight and accountability.
While the marketing agency took steps to mitigate its risks, the best additional step would be to:

A. Engage a third party to lead the procurement selection process
B. Evaluate the use of AI in the marketing industry to identify best practices
C. Negotiate an intellectual property indemnity from the technology company
D. Establish a governance committee to oversee the project

Antwort: D

Begründung:
The correct answer is D. Forming a dedicated governance committee ensures continuous oversight, role clarity, and accountability throughout the AI lifecycle.
From the AIGP ILT Guide - Governance Structures:
"Organizations using AI in high-impact scenarios should establish a governance body responsible for oversight of risk, compliance, and ethical alignment." Also reflected in AI Governance in Practice Report 2024:
"Committees support cross-functional decision-making, provide guidance for updates, and maintain accountability. This is especially critical for high-stakes applications like marketing to diverse audiences." Options A, B, and C are valid supplementary actions, but D offers a long-term and systematic governance mechanism.

101. Frage
ISO 42001 International Standard offers guidance for organizations to develop trustworthy AI management systems by:

A. Explicitly over-riding previously issued and now outdated ISO standards.
B. Requiring organizations to continuously improve the effectiveness of their AI management systems.
C. Focusing on high-risk aspects of development of AI management systems.
D. Requiring specific minimum parameters for key suppliers and key aspects of AI management systems.

Antwort: B

Begründung:
ISO 42001 guides organizations to establish AI management systems with an emphasis on continuous improvement of their effectiveness to maintain trustworthiness.

102. Frage
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

A. To apply their own judgment to the initial assessment.
B. To solicit on-going feedback on model performance.
C. To ensure they provide transparency to applicants on the model.
D. To provide a reminder of a right to appeal.

Antwort: A

Begründung:
Training underwriters ensures they understand the AI's limitations and can apply their own judgment to initial assessments, preventing over-reliance on potentially biased model outputs.

103. 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. Speed and scale.
C. Automation.
D. Adaptability.
E. Autonomy.

Antwort: A,B,C

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.

104. Frage
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