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IAPP AIGP Exam Syllabus Topics:

Section	Weight	Objectives
Foundational Concepts of AI Governance	24–28%	- Principles of responsible, ethical and trustworthy AI - Governance frameworks and core concepts - Impacts, risks and benefits of AI systems - Fundamental AI definitions, types and characteristics
Governance of AI Development	21–25%	- Model management, version control and quality assurance - Testing, validation, documentation and transparency - Requirements gathering, risk assessment and mitigation - Governance through design and lifecycle management
Governance of AI Deployment and Use	21–25%	- Lifecycle maintenance, decommissioning and updates - Implementation, monitoring and ongoing oversight - Incident response, accountability and audit - Selection, procurement and third-party management
Laws, Regulations, Standards and Frameworks	24–28%	- Alignment with data protection and privacy laws - Global and regional regulations (EU AI Act, US, etc.) - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations

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AIGP PrüfungGuide, IAPP AIGP Zertifikat - IAPP Certified Artificial Intelligence Governance Professional

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q195-Q200):

195. Frage
A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent. Which is the best step the company should take to address this development?

A. Dedicate more resources to monitor the model.
B. Conduct training for customer service teams to handle flagged transactions.
C. Maintain records of all false positives.
D. Deactivate the model until an assessment is made.

Antwort: D

Begründung:
Temporarily deactivating the model prevents further operational issues and allows a thorough assessment and retraining to reduce false positives before redeployment.

196. Frage
What is the primary purpose of conducting ethical red-teaming on an AI system?

A. To improve the model's accuracy.
B. To simulate model risk scenarios.
C. To ensure compliance with applicable law.
D. To identify security vulnerabilities.

Antwort: B

Begründung:
Ethical red-teaming involves simulating adversarial scenarios to uncover risks and vulnerabilities in AI systems before deployment.

197. 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, which of the following steps is most important in gathering the data from the clinical research partner?

A. Review the terms of use.
B. Secure the combined data sets.
C. Combine only anonymized data.
D. Perform a quality assessment.

Antwort: D

198. Frage
Which of the following considerations is the most important in mitigating the potential of bias in training and testing data?

A. Using privacy-enhancing tools to protect the data.
B. Receiving explicit consent to use the data.
C. Validating that the data is representative.
D. Assessing the sufficiency of the third-party data.

Antwort: C

Begründung:
Ensuring that training and testing data is representative of the real-world population is critical to mitigating bias in AI models.

199. Frage
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?

A. To restrict the AI tool from accessing external data sources to maintain data privacy.
B. To periodically upgrade the AI tool ' s computational resources to maintain processing efficiency.
C. To ensure that the AI tool ' s algorithm evolves independently to adapt to new data without human intervention.
D. To track and adjust the tool ' s performance in order to ensure that it meets its goals.

Antwort: D

Begründung:
The correct answer is B because continuous monitoring is a core component of AI governance that ensures systems remain effective, reliable, and aligned with their intended objectives over time. AI systems can degrade due to data drift, model drift, or changing real-world conditions, making ongoing performance tracking essential. Monitoring allows organizations to detect anomalies, biases, or performance issues and take corrective actions such as retraining or recalibration. Governance frameworks emphasize post- deployment oversight to ensure systems continue to operate safely and within acceptable risk thresholds.
Option A incorrectly suggests fully autonomous evolution without oversight, which contradicts governance principles. Options C and D address specific operational concerns but do not capture the primary purpose of continuous monitoring, which is maintaining performance, accountability, and alignment with defined goals.

200. Frage
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