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

TopicDetails
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 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 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 4
  • 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q123-Q128):

NEW QUESTION # 123
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?

Answer: C

Explanation:
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.


NEW QUESTION # 124
The best practice to manage third-party risk associated with AI systems is to create and implement policies that?

Answer: A

Explanation:
Third-party risk management for AI systems should beproportional and risk-based, involvinginitial due diligenceandongoing monitoringthat reflects thelevel of risk posedby the third party's AI system.
From theAI Governance in Practice Report 2024:
"Third-party due diligence assessments to identify possible external risk and inform selection." (p. 11)
"Legal due diligence may include verification of the personal data's lawful collection by the data broker, review of contractual obligations..." (p. 19)
* Afocuses too narrowly on financial stability.
* Cis excessive and not scalable or aligned with best practices.
* Dinappropriately separates ethical and technical risks; both must be evaluated holistically.


NEW QUESTION # 125
What is the technique to remove the effects of improperly used data from an ML system?

Answer: A

Explanation:
Model disgorgement is the technique used to remove the influence of improperly used or problematic data from a trained machine learning model.


NEW QUESTION # 126
Under the EU AI Act, which category includes AI systems that pose unacceptable risks and are generally prohibited?

Answer: D


NEW QUESTION # 127
CASE STUDY
Please use the following to answer the next question:
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party AI tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party AI-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions. One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by AI hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the AI hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
All of the following are potential negative consequences created by using the AI tool to help make hiring decisions EXCEPT:

Answer: C

Explanation:
Candidate quality is an intended positive outcome of using the AI tool, not a negative consequence; the other options represent common risks associated with AI hiring systems.


NEW QUESTION # 128
......

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