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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

A. The location(s) where data is stored.
B. The fact that an Al system is being used.
C. The human oversight measures employed.
D. How an individual may contest a decision.

Answer: A

Explanation:
Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system's operation and its impact on individuals, rather than on the technical details of data storage locations.

NEW QUESTION # 57
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

A. Expert systems.
B. Segmentation.
C. Supervised learning.
D. Generative Al.

Answer: C

Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).

NEW QUESTION # 58
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?

A. The company needs to invest in technical expertise to deploy and maintain its AI model.
B. The company has less control over the decisions made by its AI model.
C. The company is solely responsible for compliance with relevant regulations and standards of its AI model.
D. The company needs to spend more money to deploy and maintain its AI model.

Answer: C

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 # 59
The initial pilot effort for NIST's Assessing Risks and Impacts of AI (ARIA) Program is focused on risks associated with which of the following?

A. Text-to-image models.
B. Facial recognition systems.
C. Large language models.
D. Recommender systems.

Answer: C

Explanation:
The initial pilot effort of the NIST ARIA (Assessing Risks and Impacts of AI) Program focuses on evaluating risks and impacts associated with large language models (LLMs). This evaluation aims to assess how LLMs perform in real-world contexts, including their robustness, reliability, and potential societal impacts.

NEW QUESTION # 60
You asked a generative AI tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative AI tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative AI tool is an example of what is commonly called:

A. Overfitting.
B. Model collapse.
C. Prompt injection.
D. Hallucination.

Answer: D

Explanation:
The AI generating incorrect or fabricated information, like nonexistent restaurants, is referred to as hallucination.

NEW QUESTION # 61
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