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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
Exam Duration:	165 minutes
Exam Format:	Single-select, Multi-select, Scenario-based questions, Multiple-choice
Passing Score:	300 (scaled score out of 100–500)
Real Exam Qty:	100 (85 scored + ~15 unscored pilot questions)
Exam Price:	USD 799 (non-member) / USD 649 (IAPP member)
Available Languages:	English
Related Certifications:	CIPP/US CIPM CIPP/E CIPT
Certificate Validity Period:	2 years
Recommended Training:	IAPP Official AIGP Training and Resources AIGP Practice Exam (Official IAPP Store)
Exam Registration:	Pearson VUE Scheduling Portal (via IAPP account) Official IAPP AIGP Exam Registration
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

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 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.
Topic 3	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.
Topic 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q144-Q149):

NEW QUESTION # 144
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

A. Rules on General Purpose Al will apply after 6 months as a specific provision.
B. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
C. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
D. Fines for SMEs and startups will be proportionally capped.

Answer: C

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

NEW QUESTION # 145
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company's core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following best exemplifies human oversight capabilities you should enable under the relevant AI laws?

A. The company has established an AI governance team within each of its departments who are specifically required to evaluate the performance metrics, including anomalies, to ensure the AI tool is operating correctly.
B. The agentic tool that helps schedule appointments and refill prescriptions leverages Retrieval-Augmented Generation RAG to confirm there are no medication contraindications before refilling.
C. The tool requires a medical doctor to approve a diagnosis before the diagnosis is entered into the patient's medical record.
D. The tool manual requires that all physicians are required to undergo specific training regarding how to interpret the AI's output, and understand its limitations.

Answer: C

Explanation:
The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act. Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize "human-in-the-loop" controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.

NEW QUESTION # 146
What is the primary purpose of conducting ethical red-teaming on an Al system?

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

Answer: B

Explanation:
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios.
Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.

NEW QUESTION # 147
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

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

Answer: B

Explanation:
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 2024:
"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.

NEW QUESTION # 148
In the context of increasing use of AI in business operations, your company seeks to update its data privacy policies. You are tasked with evaluating the current policies and proposing necessary updates to address AI-specific risks regarding protection of personal data. Which of the following would be the most effective addition to the company's data privacy policies?

A. Request final review of the policy by senior management.
B. Request regular audits of the AI Models.
C. Require security training to employees before using AI systems.
D. Prohibit the use of AI tools within the company.

Answer: B

Explanation:
Regular audits of AI models help ensure ongoing compliance with data privacy regulations and identify AI-specific risks related to personal data protection.

NEW QUESTION # 149
......

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