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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP (International Association of Privacy Professionals)
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional Exam
Exam Number:	AIGP
Exam Duration:	165 (including 15-minute optional break)
Certificate Validity Period:	2 years
Real Exam Qty:	100
Passing Score:	300 (scaled score out of 500)
Available Languages:	English
Exam Price:	USD 649 (members) / USD 799 (non-members)
Related Certifications:	CIPP CIPT CIPM
Exam Format:	Scenario-based, Multiple-choice
Recommended Training:	Official AIGP Body of Knowledge & Study Guide IAPP Training & Resources
Exam Registration:	Pearson VUE Scheduling IAPP Official Registration
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online remote proctored or in-person at Pearson VUE test centers
Pre Condition:	No formal prerequisites; open to all professionals
Official Syllabus URL:	https://iapp.org/certify/aigp

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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 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.
Topic 4	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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q164-Q169):

NEW QUESTION # 164
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a largeorganization leastlikely touse to responsibly train stakeholders on AI terminology, strategy and governance?

A. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
B. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems
C. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
D. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture

Answer: B

Explanation:
The correct answer isA. While educating technical staff is important, expectingall technical employees to be retooled as AI developersis unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should berole-specificand align with the individual's function and responsibilities...
Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report2025supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus,broad development training for all technical employeesis the least practical and least likely approach.

NEW QUESTION # 165
Testing data is defined as a subset of data that is used to?

A. Assess a model's on-going performance in production.
B. Evaluate a model's handling of randomized edge cases.
C. Enable a model to discover and learn patterns.
D. Provide a robust evaluation of a final model.

Answer: D

Explanation:
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.

NEW QUESTION # 166
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The technology company has also addressed environmental concerns and societal harms.
Which of the following results would be considered biased outputs from this AI system EXCEPT?

A. The images of female workers are hyper-sexualized
B. The content generated for minority construction workers is insufficient
C. The advertising text generated for female audiences focuses on color and style
D. The generated ads are sent to construction companies, not individual workers

Answer: D

Explanation:
The correct answer isA. Sending ads to construction companies (business entities) rather than individual workers isa business targeting decision, not inherently a biased AI output.
From the AIGP ILT Participant Guide - Bias & Fairness Module:
"Biased outputs often include stereotyping, exclusion of underrepresented groups, or reinforcing harmful societal assumptions." Examples likeinsufficient representation of minority groupsorgender-stereotyping in visuals or languageare typical manifestations of bias.
AI Governance in Practice Report2025also notes:
"Bias in generative models may manifest in representation gaps, stereotyping, or unequal performance across demographic groups." Option A, by contrast, describes adistribution strategy, not a bias generated by the AI model.

NEW QUESTION # 167
ISO 42001 International Standard offers guidance for organizations to develop trustworthy AI management systems by:

A. Explicitly over-riding previously issued and now outdated ISO standards.
B. Focusing on high-risk aspects of development of AI management systems.
C. Requiring organizations to continuously improve the effectiveness of their AI management systems.
D. Requiring specific minimum parameters for key suppliers and key aspects of AI management systems.

Answer: C

Explanation:
ISO 42001 guides organizations to establish AI management systems with an emphasis on continuous improvement of their effectiveness to maintain trustworthiness.

NEW QUESTION # 168
An AI start-up is developing a system for automated loan approvals. The team wants to minimize risks of bias and regulatory non-compliance. They have already identified potential stakeholders, including regulators and consumer groups.
What is the most appropriate sequence of next steps?

A. Perform a probability/severity analysis,
Apply a risk mitigation hierarchy to address the most severe risks,
Conduct pre-deployment pilot testing.
B. Enable explainability tools to reassure users,
Launch pilot,
Map risks during deployment.
C. Conduct harm analysis,
Benchmark system performance,
Proceed to deployment if the system performs similarly.
D. Launch a small-scale pilot,
Gather user feedback,
Afterward analyze harms.

Answer: A

Explanation:
The correct answer is C because it follows a structured, risk-based AI governance approach aligned with best practices and regulatory expectations. AI governance frameworks emphasize identifying and assessing risks through probability and severity analysis before deployment. This allows organizations to prioritize the most critical risks, such as bias and discrimination in loan approvals. Applying a risk mitigation hierarchy ensures that controls are implemented proactively rather than reactively. Only after risks are assessed and mitigated should pilot testing occur to validate system performance in a controlled environment. Other options either delay risk assessment until after deployment or focus on limited aspects like benchmarking or explainability without addressing core risk management principles. A proactive, risk-first approach is essential for compliance, fairness, and responsible AI deployment.

NEW QUESTION # 169
......

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