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IAPP AIGP Prüfungsplan:
| Thema | Einzelheiten |
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| 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.
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| 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.
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| 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.
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| 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 Prüfungsfragen Prüfungsvorbereitungen 2026: IAPP Certified Artificial Intelligence Governance Professional - Zertifizierungsprüfung IAPP AIGP in Deutsch Englisch pdf downloaden
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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q142-Q147):
142. Frage
What are the roles and responsibilities of deployers of a proprietary model? (Choose three.)
- A. Ethical design.
- B. Technical performance.
- C. Regulatory compliance.
- D. System documentation.
- E. Ethical testing.
Antwort: B,C,E
Begründung:
Deployers of a proprietary model are responsible for ethical testing, ensuring technical performance, and maintaining regulatory compliance when using the model in their environment and for their use case.
143. 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.
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is?
- A. Reputational risk.
- B. Privacy risk.
- C. Security risk.
- D. Operational risk.
Antwort: B
Begründung:
Combining datasets, even if de-identified, increases the risk of re-identification and other privacy concerns, making privacy risk the most significant in this context.
144. Frage
What is a key opportunity for companies deploying proprietary Agentic AI models?
- A. Increased immunity from intellectual property claims.
- B. Reduced need for internal AI governance.
- C. Transferring risk to the cloud vendor.
- D. Greater ability to customize guardrails.
Antwort: D
Begründung:
C is the strongest answer because proprietary deployment can give an organization greater control over system architecture, permissions, agent behavior, data access, escalation rules, and technical guardrails. This control is especially valuable for agentic AI, where systems may autonomously invoke tools, access organizational data, and execute multistep workflows. IAPP guidance emphasizes that agentic systems require purpose-specific guardrails, including controls limiting the data and actions available to an agent. Proprietary control can therefore enable safeguards to be tailored closely to organizational risk tolerances and business requirements. It does not provide immunity from intellectual-property claims, eliminating A. Organizations cannot simply transfer accountability or all risk to a cloud provider, eliminating B. Greater agent autonomy actually increases the importance of internal governance, monitoring, authorization, and human oversight rather than reducing it, eliminating D.
145. Frage
Which of the following may be permissible uses of an AI system under the EU AI Act EXCEPT?
- A. Using biometrics in abduction cases
- B. Detecting emotions in a workplace for employee morale
- C. Improving the response time for emergency services
- D. Detecting emotions in a telemedicine session
Antwort: B
Begründung:
The correct answer is D. Emotion recognition in the workplace is flagged as unacceptable or highly restricted under the EU AI Act due to its intrusive nature and potential for misuse.
From the AIGP ILT Guide - EU AI Act Training Module:
"AI systems that monitor individuals' emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5." AI Governance in Practice Report 2024 supports this interpretation:
"Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted." Other uses listed-such as emergency response or emotion detection in healthcare-may fall under lawful and beneficial uses, especially when justified by public interest.
146. Frage
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because:
- A. It is less expensive to run.
- B. It may be better suited for applications requiring customization.
- C. It can handle voice commands and is more suitable for phone-based customer support.
- D. It is built for large-scale, complex dialogues and would be more effective in handling high-volume customer inquiries.
Antwort: B
Begründung:
Open-source models can be customized extensively, making them better suited for applications requiring specific adaptations.
147. Frage
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