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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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 (Q205-Q210):

NEW QUESTION # 205
Which of the following use cases would be best served by a non-AI solution?

Answer: A

Explanation:
The correct answer is A because developing a social media presence typically does not require AI and can be effectively achieved through traditional digital marketing strategies, content planning, and manual engagement. AI governance principles emphasize that organizations should first determine whether AI is necessary or proportionate to the problem being solved. Using AI where simpler, deterministic, or human- driven solutions suffice can introduce unnecessary complexity, cost, and risk. In contrast, options B, C, and D involve tasks such as campaign optimization, personalization, and automation, which benefit significantly from AI capabilities like pattern recognition, predictive analytics, and natural language processing.
Responsible AI governance encourages a "fit-for-purpose" approach, ensuring AI is only deployed when it provides clear added value over non-AI alternatives.


NEW QUESTION # 206
All of the following are elements of establishing a global Al governance infrastructure EXCEPT?

Answer: A

Explanation:
Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.


NEW QUESTION # 207
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT:

Answer: A


NEW QUESTION # 208
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

Answer: C

Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.


NEW QUESTION # 209
In the machine learning context, feature engineering is the process of?

Answer: B

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 # 210
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