AIGP Prüfungsunterlagen, AIGP Exam

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IAPP AIGP Prüfungsplan:

Thema	Einzelheiten
Thema 1	Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
Thema 2	Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
Thema 3	Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.

IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q77-Q82):

77. Frage
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. What business purpose it will serve.
B. How frequently it will be updated.
C. Whether its training data is disclosed.
D. Whether its system card identifies risks.

Antwort: A

Begründung:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

78. Frage
The best method to ensure a comprehensive identification of risks for a new AI model is?

A. Red teaming.
B. An impact assessment.
C. An environmental scan.
D. Integration testing.

Antwort: B

Begründung:
The most comprehensive way to identify a full range of risks - legal, ethical, operational, and societal - for a new AI model is through aformal impact assessment, such as aData Protection Impact Assessment (DPIA)orAlgorithmic Impact Assessment.
From theAI Governance in Practice Report 2024:
"Risk-based approaches are often distilled into organizational risk management efforts, which put impact assessments at the heart of deciding whether harm can be reduced." (p. 29)
"DPIAs... help organizations identify, analyze and minimize data-related risks and demonstrate accountability." (p. 30)
* A. Environmental scanis too general.
* B. Red teamingis useful for adversarial risk but not broad.
* C. Integration testingfocuses on technical/system compatibility, not overall risk.

79. Frage
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?

A. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text.
B. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate.
C. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B.
D. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources.

Antwort: A

Begründung:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.

80. Frage
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?

A. Implementing technical and organizational measures
B. Conducting a Data Protection Impact Assessment (DPIA) / Privacy Impact Assessment (PIA)
C. Ensuring that third-party processors are based in the same country as the company
D. Allowing data subject access requests (DSARs)

Antwort: C

Begründung:
The correct answer is A. While location of processors may have implications (such as for data transfers under GDPR), there is no absolute requirement that third-party processors be based in the same country.
From the AI Governance in Practice Report 2024 and ILT Guide:
"Data controllers are responsible for ensuring that third-party processors have adequate protections, but not necessarily that they reside in the same jurisdiction. What is required is legal safeguards (e.g., SCCs) for international transfers, not same-country location." In contrast, DPIAs, DSARs, and implementation of technical/organizational safeguards are explicitly required under GDPR and responsible AI frameworks.

81. Frage
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?

A. When use of the system causes or is likely to cause material harm.
B. When the algorithmic impact assessment has been completed.
C. Upon release of a new version of the system.
D. Upon initial deployment of the system.

Antwort: D

Begründung:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.

82. Frage
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