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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q216-Q221):

NEW QUESTION # 216
All of the following are commonly adopted processes and policies in reducing potential risks introduced by third-party AI tools or applications EXCEPT:

A. Including clauses in the procurement agreement for buyers of generative AI tools to put certain liabilities on the tool supplier.
B. Requiring new use cases of the generative AI tools or applications to be reviewed and approved by the generative AI governance body.
C. Requiring an independent third-party bias audit for third-party generative AI tools.
D. Allowing publicly available information and personally identifiable information (pii) to be incorporated into the prompt.

Answer: D

Explanation:
Allowing publicly available information and personally identifiable information to be incorporated into prompts increases risk rather than reducing it, making it the least aligned with common risk- mitigation practices for third-party AI tools.

NEW QUESTION # 217
According to the GDPR's transparency principle, when an AI system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

A. The contact details of the data protection officer and the data protection national authority.
B. The existence of automated decision-making and meaningful information on its logic and consequences.
C. The data protection impact assessments carried out on the AI system and legal bases for processing.
D. The personal data used during processing, including inferences drawn by the AI system about the data.

Answer: B

Explanation:
GDPR's transparency principle mandates that data subjects be informed about the existence of automated decision-making, along with meaningful information about the logic involved and the potential consequences.

NEW QUESTION # 218
In the machine learning context, feature engineering is the process of:

A. Creating learning schema for a model to apply.
B. Extracting attributes and variables from raw data.
C. Developing guidelines to train and test a model.
D. Converting raw data into clean data.

Answer: B

Explanation:
Feature engineering involves extracting and transforming relevant attributes or variables from raw data to improve model performance.

NEW QUESTION # 219
Why must AI governance policies address third-party AI systems?

A. Third-party models are always open source
B. Liability may still attach to deployers
C. Third parties are exempt from regulation
D. External systems cannot cause harm

Answer: B

NEW QUESTION # 220
During the planning and design phases of the AI development life cycle, bias can be reduced by all of the following EXCEPT:

A. Human oversight.
B. Feature selection.
C. Stakeholder involvement.
D. Data collection.

Answer: A

Explanation:
Human oversight is primarily a mitigation strategy during deployment and operation phases, whereas bias reduction during planning and design focuses on stakeholder involvement, feature selection, and data collection.

NEW QUESTION # 221
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