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[General] AIGP Reliable Exam Materials - AIGP Knowledge Points

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【General】 AIGP Reliable Exam Materials - AIGP Knowledge Points

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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 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 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 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 (Q147-Q152):NEW QUESTION # 147
In the machine learning context, feature engineering is the process of?
  • A. Converting raw data into clean data.
  • B. Creating learning schema for a model apply.
  • C. Extracting attributes and variables from raw data.
  • D. Developing guidelines to train and test a model.
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 # 148
Which of the following are not considered biometric data under U.S. privacy laws?
  • A. GPS location of a user's fitness watch
  • B. Iris scans
  • C. Walking gait
  • D. Keystroke dynamics
Answer: A
Explanation:
The correct answer is D. GPS location data is not biometric data-it is considered geolocation data, which is personal data but not biometric under most U.S. laws.
From the AIGP ILT Guide (Data Privacy Module):
"Biometric data includes measurable biological or behavioral characteristics such as iris scans, facial recognition, voice prints, and keystroke patterns when used for identification." AI Governance in Practice Report 2024 (Privacy and Data Protection section):
"Location data, while sensitive, is not considered biometric unless it's tied to a uniquely identifying biological trait." Thus, GPS location data, while potentially sensitive, is not classified as biometric.

NEW QUESTION # 149
All of the following are potential benefits of using private over public LLMs EXCEPT?
  • A. Confirmation of security and confidentiality.
  • B. Reduction in time taken for data validation and verification.
  • C. Reduction in possibility of hallucinated information.
  • D. Application for specific use cases within the enterprise.
Answer: B
Explanation:
Private LLMs offer advantages likecustomizability,reduced hallucination,confidentiality, andalignment with enterprise-specific tasks, but theydo not inherently reduce the time or effortneeded fordata validation or verification- which remains an essential step regardless of model privacy.
From the AI risk and quality sections:
"Ensuring the quality of the data... is highly contextual and must be validated regardless of the model's deployment environment." (p. 17)
* B, C, Dare legitimate benefits of private LLMs.
* Ais incorrect - validation still requires time and resources.

NEW QUESTION # 150
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
Which stakeholder is responsible for the lawful collection of data used to train the foundational AI model?
  • A. The tech company
  • B. The marketing agency
  • C. The marketing agency's client
  • D. The data aggregator
Answer: A
Explanation:
The correct answer isB - The tech company. The party thatdevelops and trains the foundational modelis responsible for ensuring thelawful collection of training data.
From the AIGP ILT Guide - Foundational Models & Data Governance:
"Responsibility for the lawfulness of data collection typically lies with the party that trains the model- usually the provider or developer of the foundational model." AI Governance in Practice Report2025confirms:
"General Purpose AI providers are required to ensure that training data is lawfully acquired, including compliance with intellectual property and privacy requirements." The marketing agency is only auserordownstream integrator, not responsible for original data collection.

NEW QUESTION # 151
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.
Which of the following laws is NOT relevant to this use case?
  • A. Fair Credit Reporting Act.
  • B. Fair Housing Act.
  • C. Title VII of the Civil Rights Act of 1964.
  • D. Equal Credit Opportunity Act.
Answer: C
Explanation:
The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

NEW QUESTION # 152
......
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