IAPP AIGP Test Topics Pdf, AIGP Exam Actual Questions

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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 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 3
  • 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 4
  • 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q65-Q70):

NEW QUESTION # 65
A UK company has designed a facial recognition model to support border control.
The EU AI Act would apply to the model in all of the following situations EXCEPT if?

Answer: C

Explanation:
TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systemsimpact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or usedentirely outside of the EU-such as in theUK, unless they affect the EU market or individuals.
From theAI Governance in Practice Report2025:
"The act imposes regulatory obligations... depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems." (p. 7)
"The EU AI Act is the world's first comprehensive AI regulation... requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market." (p. 7-8) Thus:
* A. Open source releasedoes not exclude applicability if deployed in the EU.
* B. Deployment at an EU borderclearly invokes jurisdiction.
* D. Training by an EU companycreates jurisdictional links.
* C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.


NEW QUESTION # 66
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: A

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.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing user rights and ethical considerations in AI.


NEW QUESTION # 67
A Canadian company is developing an AI solution to evaluate candidates in the course of job interviews. Before offering the AI solution in the EU market, the company must take all of the following steps EXCEPT:

Answer: C

Explanation:
While bias audits are important, the EU AI Act does not mandate engaging a third-party auditor; registration, risk management systems, and technical documentation are required.


NEW QUESTION # 68
In the machine learning context, feature engineering is the process of?

Answer: C

Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.


NEW QUESTION # 69
Scenario:
A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act.
The regulator is considering penalties under the enforcement provisions of the regulation.
According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to €
15 million or 3% of annual worldwide turnover(whichever is higher)?

Answer: B

Explanation:
The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation.
A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €
15 million or 3% of annual global turnover.
From the AIGP ILT Guide - EU AI Act Module:
"Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover." AI Governance in Practice Report 2024 supports this:
"Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article
71(3) of the EU AI Act."
Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.


NEW QUESTION # 70
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