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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 (Q167-Q172):

NEW QUESTION # 167
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
From a governance perspective, which of the following correctly describe the responsibilities of AI developers or deployers?

A. AI developers are responsible for reducing biases in the system, such as discrimination against certain demographics.
B. AI developers must provide clear documentation explaining how the system functions for its operators.
C. AI deployers must handle ongoing monitoring and auditing of the AI system.
D. AI developers remain fully liable for the AI system's compliance with regulations even after deployment by others.
E. AI deployers must conduct independent bias assessments prior to release of a system.

Answer: A,B,C

Explanation:
The correct answers are A, C, and E because they reflect the shared but distinct responsibilities between AI developers and deployers in governance frameworks. Developers are responsible for designing systems that minimize bias and ensuring fairness during development. They are also required to provide clear documentation, including instructions for use, system capabilities, and limitations, to support transparency and proper operation. Deployers, on the other hand, are responsible for overseeing the system once it is in use, including continuous monitoring, auditing, and ensuring it performs as intended in real-world conditions. Option B is incorrect because liability is typically shared and depends on roles and context, not solely on developers. Option D is incorrect because independent bias assessments are not always strictly required of deployers before release.

NEW QUESTION # 168
Which of the following AI uses is best described as human-centric?

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

Answer: C

Explanation:
Virtual assistants that personalize education based on individual abilities and needs focus directly on enhancing human learning experiences, making this use human-centric.

NEW QUESTION # 169
All of the following are examples of biometric data in the US EXCEPT?

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

Answer: B

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 Report2025and 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 # 170
A company is creating a mobile app to enable individuals to upload images and videos, and analyze this data using ML to provide lifestyle improvement recommendations. The sign-up form has the following data fields:
1. First name
2. Last name
3. Mobile number
4. Email ID
5. New password
6. Date of birth
7. Gender
In addition, the app obtains a device's IP address and location information while in use.
What GDPR privacy principles does this violate?

A. Transparency and Accuracy.
B. Accountability and Lawfulness.
C. Integrity and Confidentiality.
D. Purpose Limitation and Data Minimization.

Answer: D

Explanation:
Collecting more personal data than necessary violates GDPR principles of Purpose Limitation (using data only for specific purposes) and Data Minimization (collecting only what is needed).

NEW QUESTION # 171
Which of the following is NOT a common type of machine learning?

A. Deep learning.
B. Unsupervised learning.
C. Cognitive learning.
D. Reinforcement learning.

Answer: C

Explanation:
Cognitive learning is not a recognized common type of machine learning, unlike deep learning, unsupervised learning, and reinforcement learning.

NEW QUESTION # 172
......

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