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IAPP AIGP Exam Syllabus Topics:

TopicDetails
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 (Q145-Q150):

NEW QUESTION # 145
All of the following are reasons to deploy a challenger AI model in addition to a champion AI model EXCEPT to:

Answer: A

Explanation:
Retraining the champion model is a maintenance activity, not a reason to deploy a challenger model; challenger models provide alternative options for comparison and testing.


NEW QUESTION # 146
The White House Executive Order from November 2023 requires companies that develop dual- use foundation models to provide reports to the federal government about all of the following EXCEPT:

Answer: C

Explanation:
Under the White House Executive Order on Artificial Intelligence (EO 14110), companies developing dual-use foundation models are required to report specific information to the federal government. These requirements include:
1. Ongoing or planned activities related to training, developing, or producing dual-use foundation models, including physical and cybersecurity protections taken to assure the integrity of the training process against sophisticated threats.
2. Ownership and possession of the model weights of any dual-use foundation models, and the physical and cybersecurity measures taken to protect those model weights.
3. The results of any developed dual-use foundation model's performance in relevant AI red-team testing, including a description of any associated measures the company has taken to meet safety objectives, such as mitigations to improve performance on these red-team tests and strengthen overall model security.
4. Other information pertaining to the safety and reliability of dual-use foundation models, or activities or risks that present concerns to U.S. national security.
Notably, the EO does not mandate companies to conduct or report on environmental impact studies for each dual-use foundation model. Therefore, option C is not a required reporting obligation under the Executive Order.


NEW QUESTION # 147
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model. Which of the following is NOT a best practice to apply during the testing?

Answer: C

Explanation:
Minimizing human involvement during testing is not a best practice; human oversight is critical to validate model behavior and ensure ethical and accurate performance.


NEW QUESTION # 148
What is the most important purpose of a validation data set when developing a machine learning model?

Answer: A

Explanation:
The validation dataset is primarily used during training to tune and optimize the model's parameters before final evaluation.


NEW QUESTION # 149
To assist its internal recruiters with filtering job applications, a company decides to develop in- house an AI model for the screening and ranking of job applicants' resumes. Which of the following is a unique issue this company might face compared to using a third-party AI service?

Answer: A

Explanation:
When developing an AI model in-house, the company bears sole responsibility for ensuring compliance with relevant regulations and standards, unlike when using third-party services where some responsibility may be shared.


NEW QUESTION # 150
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