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| Topic | Details |
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| 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.
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| 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.
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| 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.
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| 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 (Q63-Q68):
NEW QUESTION # 63
In the machine learning context, feature engineering is the process of?
- A. Creating learning schema for a model apply.
- B. Extracting attributes and variables from raw data.
- C. Developing guidelines to train and test a model.
- D. Converting raw data into clean data.
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 # 64
Which of the following is an obligation of an importer of high risk AI systems under the EU AI Act?
- A. Conduct a data protection impact assessment.
- B. Verify the Declaration of Conformity.
- C. Affix the CE marking.
- D. Provide technical documentation.
Answer: B
Explanation:
Importers of high-risk AI systems must verify that the system bears the CE marking and that the EU Declaration of Conformity has been issued by the provider. This is a direct obligation under the EU AI Act.
NEW QUESTION # 65
According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative Al as a workplace tool?
- A. Limit access to specific uses of generative Al.
- B. Impose a general ban on the use of generative Al.
- C. Impose a ban on the use of generative Al in agencies that protect national security.
- D. Limit access of generative Al to engineers and developers.
Answer: A
Explanation:
The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and risk management, and the White House Executive Order of November 2023.
NEW QUESTION # 66
Before training an AI model, which activity best supports governance and risk mitigation?
- A. Deploying the model in a test environment
- B. Selecting the largest available dataset
- C. Publishing a transparency notice
- D. Performing an AI impact assessment
Answer: D
NEW QUESTION # 67
An organization establishes a cross-functional AI governance committee including legal, engineering, compliance, and HR teams. What is the primary purpose of this approach?
- A. To centralize technical decision-making
- B. To accelerate AI deployment timelines
- C. To ensure diverse expertise and risk perspectives
- D. To reduce the need for external audits
Answer: C
NEW QUESTION # 68
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