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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q121-Q126):

NEW QUESTION # 121
In the machine learning context, feature engineering is the process of?

A. Developing guidelines to train and test a model.
B. Converting raw data into clean data.
C. Creating learning schema for a model apply.
D. Extracting attributes and variables from raw data.

Answer: D

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 # 122
What is most likely the first action that a developer takes to map, plan and scope an AI project?

A. Define the business case and perform a cost/benefit analysis answering the question of "why AI?"
B. Use a test, evaluation, verification, validation (TEVV) process.
C. Determine feasibility and optionality of redress.
D. Perform an algorithmic impact assessment leveraging PIAs.

Answer: A

Explanation:
The first action is to clearly define the business case and conduct a cost/benefit analysis to justify the use of AI and set project scope.

NEW QUESTION # 123
The use of paid generative AI public tools is appealing because? (Choose three.)

A. They have additional privacy and security controls.
B. They provide transparency in models and in decision-making.
C. They eliminate concerns about the data used to generate outputs.
D. They are convenient to adopt.
E. They have frequent enhancements of new features.

Answer: A,D,E

Explanation:
Paid generative AI tools are appealing because they are convenient to adopt, often include enhanced privacy and security controls, and receive frequent feature updates.

NEW QUESTION # 124
Which of the following Al uses is best described as human-centric?

A. Machine learning is used for demand forecasting and inventory management, ensuring that consumers can find products they want when they want them.
B. Autonomous robots are used to move products within a warehouse, allowing human workers to reduce physical strain and alleviate monotony.
C. Pattern recognition algorithms are used to improve the accuracy of weather predictions, which benefits many industries and everyday life.
D. Virtual assistants are used adapt educational content and teaching methods to individuals, offering personalized recommendations based on ability and needs.

Answer: D

Explanation:
Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.

NEW QUESTION # 125
Which of the following use cases would be best served by a non-AI solution?

A. An e-commerce provider wants to make personalized recommendations.
B. A non-profit wants to develop a social media presence.
C. A customer service agency wants to automate answers to common questions.
D. A business analyst wants to develop advertising campaigns.

Answer: B

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 # 126
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