AIGP Reliable Exam Topics & AIGP Official Study Guide

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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
Available Languages:English
Exam Format:Scenario-based, Multiple-choice
Real Exam Qty:100
Related Certifications:CIPP
CIPT
CIPM
Exam Duration:165 (including 15-minute optional break)
Exam Price:USD 649 (members) / USD 799 (non-members)
Passing Score:300 (scaled score out of 500)
Certificate Validity Period:2 years
Recommended Training:IAPP Training & Resources
Official AIGP Body of Knowledge & Study Guide
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

>> AIGP Reliable Exam Topics <<

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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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
All of the following issues are unique for proprietary AI model deployments EXCEPT:

Answer: D

Explanation:
The potential for bias is not unique to proprietary AI models; it applies to all AI systems regardless of ownership or deployment type.


NEW QUESTION # 73
What is the primary purpose of conducting ethical red-teaming on an Al system?

Answer: A

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 # 74
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?

Answer: C

Explanation:
The correct answer is A. Pre- and post-deployment testing ensures bias, accuracy, and fairness are evaluated and corrected as needed, which is essential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report 2024 (Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).


NEW QUESTION # 75
ISO/IEC 22989 and 42001 can be valuable resources for AI Governance professionals in all of the following ways EXCEPT:

Answer: A

Explanation:
ISO/IEC 22989 and 42001 provide foundational concepts, terminology, and governance guidance for AI systems, but they do not address the detailed, specific processes required for managing procurement with third-party AI providers.


NEW QUESTION # 76
Testing data is defined as a subset of data that is used to:

Answer: A

Explanation:
Testing data is used to provide an unbiased evaluation of the final model's performance before deployment.


NEW QUESTION # 77
......

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