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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 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 (Q26-Q31):

NEW QUESTION # 26
Retraining an LLM can be necessary for all of the following reasons EXCEPT:

A. To account for new interpretations of the same data.
B. To adjust the model's hyperparameters to a specific use case.
C. To ensure interpretability of the model's predictions.
D. To minimize degradation in prediction accuracy due to changes in data.

Answer: C

Explanation:
Retraining focuses on adapting the model to data changes or specific use cases, but it does not directly ensure interpretability of predictions, which involves different methods.

NEW QUESTION # 27
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 deployers must conduct independent bias assessments prior to release of a system.
B. AI developers remain fully liable for the AI system ' s compliance with regulations even after deployment by others.
C. AI developers are responsible for reducing biases in the system, such as discrimination against certain demographics.
D. AI deployers must handle ongoing monitoring and auditing of the AI system.
E. AI developers must provide clear documentation explaining how the system functions for its operators.

Answer: C,D,E

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 # 28
Please select 3 of the 5 options below. No partial credit will be given.
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT?

A. Autonomy.
B. Speed and scale.
C. Superintelligence.
D. Automation.
E. Adaptability.

Answer: B,C,D

Explanation:
AI governance frameworks consistently define the"unique characteristics"of AI that create novel governance challenges.
Across the OECD AI Principles, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, the key characteristics requiring governance are:
* Autonomy- AI systems act without direct human intervention and take context-dependent decisions.
* Adaptability- AI systems learn, evolve, and update their behavior over time, making outputs non- deterministic.
These traits fundamentally distinguish AI from traditional software and shape all major governance activities (risk assessment, monitoring, assurance, accountability, alignment, etc.).
Below is why each option is correct or incorrect:
A). Autonomy - NOT selected
Reason:
Autonomy is repeatedly cited as one of the defining characteristics that necessitates governance.
NIST AI RMF references the risks ofautonomous behavior.
ISO/IEC 42001 emphasizes governance controls for "systems operating with varying levels of autonomy." Thus,Autonomy IS a unique characteristic, so it isnotan answer.
B). Automation - SELECTED
Reason:
"Automation" is not a unique AI property. Automation predates AI and applies to robotics, scripts, business process automation, etc.
AI governance literature doesnotclassify automation as a unique AI-specific characteristic.
Automation is aresultof AI, but not adistinguishing property.
Therefore,Automation is correctly selected as NOT a unique AI characteristic.
C). Adaptability - NOT selected
Reason:
Adaptability (systems that update, learn, or change behavior) is recognized universally as a core distinctive trait of AI.
EU AI Act emphasizes governance requirements for "adaptive systems."
NIST AI RMF highlights "learning and emergent behavior."
Thus,Adaptability IS a unique characteristic, so it shouldnotbe chosen as an exception.
D). Speed and scale - SELECTED
Reason:
While AI can operate at high speed and scale,these are not unique characteristics of AI.
Traditional computing systems, cloud workloads, and automation pipelines operate at similar scale.
AI governance documents mention speed/scale as arisk amplifier, not a defining characteristic.
Therefore,Speed and scaleshould be selected.
E). Superintelligence - SELECTED
Reason:
"Superintelligence" is not a characteristic of current AI systems and isnot referenced as a governance-relevant attributein mainstream policy documents (OECD, NIST, ISO, EU AI Act).
It is speculative rather than a defining feature.
Therefore, it is correctly chosen as NOT a unique characteristic requiring governance today.

NEW QUESTION # 29
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Identify known edge cases to monitor post-deployment.
B. Define a model-validation methodology.
C. Perform a readiness assessment.
D. Document maintenance teams and processes.

Answer: C

Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance.
It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

NEW QUESTION # 30
Which of the following is an example of a high-risk application under the EU Al Act?

A. A resume scanning tool that ranks applicants.
B. A government-run social scoring tool.
C. An Al-enabled inventory management tool.
D. A customer service chatbot tool.

Answer: B

Explanation:
The EU AI Act categorizes certain applications of AI as high-risk due to their potential impact on fundamental rights and safety. High-risk applications include those used in critical areas such as employment, education, and essential public services. A government-run social scoring tool, which assesses individuals based on their social behavior or perceived trustworthiness, falls under this category because of its profound implications for privacy, fairness, and individual rights. This contrasts with other AI applications like resume scanning tools or customer service chatbots, which are generally not classified as high-risk under the EU AI Act.

NEW QUESTION # 31
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