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The IAPP Certified Artificial Intelligence Governance Professional (AIGP) practice test is being offered in three different formats. These IAPP AIGP exam questions formats are PDF dumps files, web-based practice test software, and desktop practice test software. All these IAPP AIGP Exam Dumps formats contain real, updated, and error-free IAPP Certified Artificial Intelligence Governance Professional (AIGP) exam questions that prepare you for the final AIGP exam.

IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q172-Q177):

NEW QUESTION # 172
All of the following analyses are part of a deactivating risk strategy EXCEPT:

A. Understanding algorithmic methodology in AI model
B. Evaluating business continuity and financial risks.
C. Evaluating up and downstream system dependencies.
D. Understanding regulatory requirements related to data retention.

Answer: A

Explanation:
Understanding algorithmic methodology is part of assessing model design and functionality, not typically a focus of deactivating risk strategies, which center on operational, regulatory, and system continuity risks.

NEW QUESTION # 173
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?

A. To comply with applicable law.
B. To determine the explainability of the Al system.
C. To evaluate the reliability of the Al system.
D. To assist the fairness of the Al system.

Answer: D

Explanation:
The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.

NEW QUESTION # 174
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.

Answer: A

Explanation:
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 2025:
"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.

NEW QUESTION # 175
Why is it important that conformity requirements are satisfied before an AI system is released into production?

A. To comply with legal and regulatory standards, ensuring the AI system is safe and trustworthy.
B. To ensure the AI system is easy for end-users to operate.
C. To guarantee interoperability of the AI system across multiple platforms and environments.
D. To ensure the visual design is fit for purpose.

Answer: A

Explanation:
The correct answer is D because conformity requirements are primarily intended to ensure that AI systems meet applicable legal, regulatory, and safety standards before deployment. AI governance frameworks, including the EU AI Act and international standards, require conformity assessments to verify that systems are safe, reliable, and compliant with risk management, documentation, and performance obligations. These assessments help identify and mitigate risks prior to market release, particularly for high-risk AI systems that may impact individuals' rights, health, or safety. Conformity ensures accountability, transparency, and trustworthiness, which are central principles of responsible AI governance. The other options relate to usability or technical considerations, but they do not address the pri mary purpose of conformity assessments, which is regulatory compliance and risk mitigation prior to deployment.

NEW QUESTION # 176
Which of the following is a foundational characteristic of effective AI governance?

A. Engagement of a cross-functional team
B. Thorough reviews of a company's public filings with experts
C. Reliance on tested vendor management processes
D. Uniform policies and procedures across developer, deployer and user roles

Answer: A

Explanation:
The correct answer is Engagement of a cross-functional team. Effective AI governance requires collaboration among various organizational functions including legal, compliance, IT, ethics, and data science.
From the AIGP Body of Knowledge:
"AI governance cannot be siloed-it requires input and oversight from across departments... A cross- functional team ensures that ethical, technical, legal, and operational risks are all appropriately managed." Also confirmed in the ILT Participant Guide:
"Cross-functional teams allow organizations to bring in different perspectives... Legal, compliance, and technical experts must work together to ensure responsible AI outcomes."

NEW QUESTION # 177
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