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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q10-Q15):

NEW QUESTION # 10
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI 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 AI system works by surveying 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 to 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 AI 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 AI 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 AI 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?

Answer: A

Explanation:
Requesting information about dataset diversity helps assess whether the AI system treats different demographic groups fairly, mitigating bias and discrimination risks.


NEW QUESTION # 11
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:

Answer: C

Explanation:
NIST's ARIA program is designed to provide organizations with practical resources, guidance, and methodologies to assess and manage the risks and impacts of AI systems.


NEW QUESTION # 12
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT: (Choose three.)

Answer: A,B,D

Explanation:
The commonly recognized unique characteristics of AI that drive the need for comprehensive governance are autonomy and adaptability.
Automation, speed and scale, and superintelligence are not considered core unique characteristics for governance purposes in standard AI governance frameworks, making them the correct exceptions.


NEW QUESTION # 13
Scenario:
A public sector agency is reviewing proposed AI use cases for improving services. It wants to prioritize implementations that deliver value butminimize unintended negative consequences.
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

Answer: D

Explanation:
The correct answer isA. While TEVV is important inlater lifecycle phases, it isnot the primary considerationwhen evaluating and prioritizinguse cases.
From the AIGP Body of Knowledge - Use Case Assessment Module:
"Use case evaluation focuses on value, impact, fairness, and accessibility-technical testing considerations come later." ILT Guide confirms:
"Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization." Thus,Ais not a top-level consideration during use caseselection.


NEW QUESTION # 14
After initially deploying a third-party AI model, you learn the developer has released a new version. As deployer of this third-party model, what should you do?

Answer: B

Explanation:
When a third-party developer releases a new model version, the deployer must first audit the updated model to determine whether the changes introduce new risks, alter performance, or affect compliance obligations before considering deployment or communicating updates.


NEW QUESTION # 15
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