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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 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 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 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 (Q111-Q116):

NEW QUESTION # 111
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight. These failures are an example of:

A. Hallucination.
B. Uncertainty.
C. Forgetting.
D. Brittleness.

Answer: D

Explanation:
Brittleness refers to AI systems failing unexpectedly when encountering slight variations or edge cases, such as sensor misinterpretation due to sunlight.

NEW QUESTION # 112
What is the primary purpose of conducting ethical red-teaming on an AI system?

A. To improve the model's accuracy.
B. To simulate model risk scenarios.
C. To identify security vulnerabilities.
D. To ensure compliance with applicable law.

Answer: B

Explanation:
Ethical red-teaming involves simulating adversarial scenarios to uncover risks and vulnerabilities in AI systems before deployment.

NEW QUESTION # 113
All of the following are examples of biometric data in the US EXCEPT?

A. Keystroke dynamics.
B. Iris scans.
C. Walking gait.
D. GPS location of a user's fitness watch.

Answer: D

Explanation:
Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.
According to definitions and discussions from theAI Governance in Practice Report 2024and U.S. privacy frameworks:
"Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition." (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.) Here's how the options relate:
* A. Iris scans- These are physical biometric identifiers.
* B. Walking gait- Behavioral biometric used increasingly in surveillance and identification.
* C. Keystroke dynamics- Behavioral biometric based on typing patterns.
* D. GPS location of a user's fitness watch- This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.

NEW QUESTION # 114
In the machine learning context, feature engineering is the process of?

A. Creating learning schema for a model apply.
B. Developing guidelines to train and test a model.
C. Converting raw data into clean data.
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 # 115
When should an ethical impact assessment for AI be performed?

A. When the system is ready for production and deployment.
B. When the number of AI system users has reached a predefined threshold.
C. At the initial stages of AI system development and continually throughout its lifecycle.
D. After the AI system has encountered ethical issues in real-world applications.

Answer: C

Explanation:
Ethical impact assessment should begin early and remain iterative throughout the AI lifecycle, making D correct. Waiting until production, a user threshold, or an actual ethical incident would make the assessment reactive rather than preventive. UNESCO describes ethical impact assessment as an " ethics by design " mechanism covering research, design, development, deployment, use, maintenance, monitoring, and eventual termination, while emphasizing its particular value during early research and design. OECD-listed assessment guidance similarly states that the first assessment should occur during design, with reassessments at development, deployment, and other significant lifecycle stages. NIST ' s AI RMF Playbook also recommends impact assessment at key stages because risks, operating conditions, stakeholders, data, and system capabilities can change over time. Continuous reassessment therefore supports proactive identification and mitigation of emerging harms.

NEW QUESTION # 116
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