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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP (International Association of Privacy Professionals)
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional (AIGP) Exam
Exam Number:	AIGP
Passing Score:	300 (scaled score out of 100–500)
Certificate Validity Period:	2 years
Real Exam Qty:	100 (85 scored + ~15 unscored pilot questions)
Exam Duration:	165 minutes
Exam Price:	USD 799 (non-member) / USD 649 (IAPP member)
Related Certifications:	CIPM CIPP/E CIPT CIPP/US
Available Languages:	English
Exam Format:	Single-select, Scenario-based questions, Multiple-choice, Multi-select
Recommended Training:	AIGP Practice Exam (Official IAPP Store) IAPP Official AIGP Training and Resources
Exam Registration:	Official IAPP AIGP Exam Registration Pearson VUE Scheduling Portal (via IAPP account)
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Computer-based exam delivered via Pearson VUE (test center or online proctored OnVUE)
Pre Condition:	None
Official Syllabus URL:	https://iapp.org/certify/aigp

>> AIGP Musterprüfungsfragen <<

AIGP Übungsmaterialien & AIGP Lernführung: IAPP Certified Artificial Intelligence Governance Professional & AIGP Lernguide

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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 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 4	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.

IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q119-Q124):

119. Frage
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

A. Establish a human-in-the-loop procedure.
B. Provide a just-in-time notice about the automated decision-making logic.
C. Define suitable measures to safeguard personal data.
D. Provide a right to review automated decision.

Antwort: A

Begründung:
The GDPR requires that individuals have the right to not be subject to decisions based solely on automated processing, including profiling, unless specific exceptions apply. One effective control is to establish a human-in-the-loop procedure (D), ensuring human oversight and the ability to contest decisions. This goes beyond just-in-time notices (A), data safeguarding (B), or review rights (C), providing a more robust mechanism to protect individuals' rights.

120. Frage
Scenario:
An organization is planning to deploy a new internal application that uses AI to make automated decisions about individuals. This application will process personal information and may affect individuals' access to certain benefits or opportunities.
Which of the following documents must be updated to ensure transparency?

A. The user privacy notice
B. The organization's website privacy notice
C. The organization's acceptable use policy
D. The organization's privacy policy

Antwort: A

Begründung:
The correct answer isD. Transparency obligations under data protection laws, such as GDPR and most AI governance frameworks, require thatusers whose data is being processedbe directly informed.
From the AIGP ILT Guide (Privacy Module):
"The user privacy notice must be updated to explain the nature of automated processing, the logic involved, and the significance and consequences for the data subject." Also, per AI Governance in Practice Report2025(Part III):
"Transparency obligations apply throughout the lifecycle of AI... Individuals must be informed about automated decision-making and profiling that may impact them." Unlike internal policies or general privacy notices,the user privacy noticeprovides direct transparency to theindividual data subjectsaffected by AI processing.

121. Frage
Training data is best defined as a subset of data that is used to:

A. Detect the initial sources of biases to mitigate prior to deployment.
B. Enable a model to detect and learn patterns.
C. Fine-tune a model to improve accuracy and prevent overfitting.
D. Resemble the structure and statistical properties of production data.

Antwort: B

Begründung:
Training data is the dataset used to enable a machine learning model to detect and learn underlying patterns, which forms the basis for its predictive capabilities.

122. Frage
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

A. The marketing agency ' s client.
B. The marketing agency.
C. The tech company.
D. The data aggregator.

Antwort: D

Begründung:
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report 2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data ' s lawful collection by the databroker..." (p. 19) This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

123. Frage
What type of organizational risk is associated with Al's resource-intensive computing demands?

A. Environmental risk.
B. Security risk.
C. Third-party risk.
D. People risk.

Antwort: A

Begründung:
AI's resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.

124. Frage
......

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