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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 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 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 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 (Q30-Q35):

NEW QUESTION # 30
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?

A. GPUs can run every task on a computer, making them more robust than CPUs.
B. GPUs run many tasks concurrently, resulting in faster processing.
C. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
D. GPUs can access memory quickly, resulting in lower latency than CPUs.

Answer: B

Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference:
AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.

NEW QUESTION # 31
The planning phase of the Al life cycle articulates all of the following EXCEPT the?

A. Choice of the architecture.
B. Objective of the model.
C. Approach to governance.
D. Context in which the model will operate.

Answer: C

Explanation:
The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.

NEW QUESTION # 32
What type of organizational risk is associated with Al's resource-intensive computing demands?

A. Security risk.
B. People risk.
C. Environmental risk.
D. Third-party risk.

Answer: C

Explanation:
AI's resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.

NEW QUESTION # 33
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.
What is the best reason the police department should continue to perform investigations even if the Al system scores an individual's likelihood of criminal activity at or above 90%?

A. Because Al systems that affect fundamental civil rights should not be fully automated.
B. Because investigations may identify additional individuals involved in the crime.
C. Because investigations may uncover information relevant to sentencing.
D. Because the department did not perform an impact assessment for this intended use.

Answer: A

Explanation:
The best reason for the police department to continue performing investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90% is that AI systems affecting fundamental civil rights should not be fully automated. Human oversight is essential to ensure that decisions impacting civil liberties are made with due consideration of context and mitigating factors that an AI might not fully appreciate. This approach ensures fairness, accountability, and adherence to legal standards. Reference: AIGP Body of Knowledge on AI Ethics and Human Oversight.

NEW QUESTION # 34
According to the GDPR, what is an effective control to prevent a determination based solely on automated decision-making?

A. Define suitable measures to safeguard personal data.
B. Provide a right to review automated decision.
C. Provide a just-in-time notice about the automated decision-making logic.
D. Establish a human-in-the-loop procedure.

Answer: D

Explanation:
GDPR requires effective controls like human-in-the-loop procedures to prevent decisions based solely on automated processing without human intervention.

NEW QUESTION # 35
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