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IAPP AIGP Exam Syllabus Topics:

Topic
Details

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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q10-Q15):

NEW QUESTION # 10
Scenario:
An organization is building a compliance program to ensure responsible AI deployment. It aims to align operations with AI risk frameworks and mitigate legal, ethical, and operational risks, while still promoting innovation.
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible AI?

A. Consulting experts to consider the ethical principles underpinning the use of AI within the organization
B. Employing a new software platform to modernize existing compliance processes across the organization
C. Meeting with and obtaining approval from senior management
D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders

Answer: B

Explanation:
The correct answer is D. While modernization through software may support efficiency, it is not a foundational or essential component of designing an integrated strategy.
From the AI Governance in Practice Report 2024:
"Integrated strategies rely on senior management support, ethical reviews, and stakeholder engagement... The use of tools and platforms may come later as an operational enhancement." Also confirmed in AIGP Body of Knowledge:
"Key components of a governance framework include leadership buy-in, ethical analysis, and stakeholder input. Tools are supporting elements-not strategic drivers."

NEW QUESTION # 11
Scenario:
An organization is evaluating different AI models for integration into its internal workflows. Before moving forward with a particular AI solution from a third-party vendor, the governance team needs to assess the ethical and operational implications of the model.
The most important policy to assess the operations of an AI model is to follow the:

A. Security policy of the model provider
B. Code of conduct policy of the model provider
C. Acceptable use policy of the model provider
D. Privacy policy of the model provider

Answer: C

Explanation:
The correct answer is A. The Acceptable Use Policy (AUP) sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.
From the AIGP ILT Guide:
"The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices." Also confirmed in the AI Governance in Practice Report 2024 (Third-Party AI Assurance section):
"Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices."

NEW QUESTION # 12
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?

A. To comply with applicable law.
B. To determine the explainability of the Al system.
C. To evaluate the reliability of the Al system.
D. To assist the fairness of the Al system.

Answer: D

Explanation:
The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.

NEW QUESTION # 13
Testing data is defined as a subset of data that is used to?

A. Provide a robust evaluation of a final model.
B. Enable a model to discover and learn patterns.
C. Assess a model's on-going performance in production.
D. Evaluate a model's handling of randomized edge cases.

Answer: A

Explanation:
Testing data is a subset of data used to provide a robust evaluation of a final model. After training the model on training data, it is essential to test its performance on unseen data (testing data) to ensure it generalizes well to new, real-world scenarios. This step helps in assessing the model's accuracy, reliability, and ability to handle various data inputs. Reference: AIGP Body of Knowledge on Model Validation and Testing.

NEW QUESTION # 14
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.
B. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
C. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
D. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Answer: D

Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals.
Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

NEW QUESTION # 15
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