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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 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 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 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 (Q209-Q214):

NEW QUESTION # 209
What is a primary liability concern when a company deploys a proprietary Agentic AI model?

A. Direct accountability for harmful or deceptive agent behavior.
B. Dependence on open-source software for orchestration.
C. Lack of available APIs for memory integration.
D. Greater public scrutiny due to external model hosting.

Answer: A

Explanation:
The correct answer is A because deploying a proprietary agentic AI model places direct responsibility on the organization for the actions and outcomes of that system. AI governance frameworks emphasize accountability as a core principle, meaning organizations must take responsibility for harms caused by their AI systems, including misleading, biased, or unsafe outputs. Agentic AI systems, which can act autonomously and make decisions, introduce heightened liability risks because their behavior may directly impact users or third parties. Regulatory approaches increasingly focus on ensuring that deployers cannot shift responsibility, especially when the system is proprietary and under their control. The other options relate to operational or technical considerations, not primary legal liability. Ensuring accountability for AI behavior is central to compliance, risk management, and maintaining trust.

NEW QUESTION # 210
You are an engineer that developed an AI-based ad recommendation tool. Which of the following should be monitored to evaluate the tool's effectiveness?

A. Algorithmic patterns, to show the model has a high degree of accuracy.
B. Input data, to ensure the ads are reaching the target audience.
C. Output data, to assess the delta between the prediction and actual ad clicks.
D. GPU performance, to evaluate the tool's robustness.

Answer: C

Explanation:
Monitoring output data and comparing predicted versus actual ad clicks provides a direct measure of the AI tool's effectiveness in recommending ads.

NEW QUESTION # 211
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
What are the roles and responsibilities of deployers of a proprietary model?

A. Technical performance.
B. Regulatory compliance.
C. Ethical testing.
D. System documentation.
E. Ethical design.

Answer: A,B,C

Explanation:
Deployers of proprietary models arenot responsible for design, but they are accountable for how the system performsin their context of use, including ensuring ethical behavior, performance, and legal compliance.
From theAI Governance in Practice Report 2024:
"Deployers of AI systems must take reasonable steps to ensure that systems are used ethically, perform safely, and align with applicable laws and standards." (p. 11-12)
"Operational governance... includes performance monitoring protocols, incident management plans, and regulatory oversight." (p. 12) Thus:
* #A. Ethical testing- Required to mitigate misuse and unintended harms.
* #B. Ethical design- Belongs todevelopers/providers, not deployers.
* #C. Technical performance- Deployers must ensure that AI performs as expected.
* #D. System documentation- This is theprovider'sobligation.
* #E. Regulatory compliance- Deployers must ensure system use complies with applicable laws.

NEW QUESTION # 212
All of the following are potential benefits of using private over public LLMs EXCEPT?

A. Confirmation of security and confidentiality.
B. Reduction in time taken for data validation and verification.
C. Application for specific use cases within the enterprise.
D. Reduction in possibility of hallucinated information.

Answer: B

Explanation:
Private LLMs offer advantages likecustomizability,reduced hallucination,confidentiality, andalignment with enterprise-specific tasks, but theydo not inherently reduce the time or effortneeded fordata validation or verification- which remains an essential step regardless of model privacy.
From the AI risk and quality sections:
"Ensuring the quality of the data... is highly contextual and must be validated regardless of the model's deployment environment." (p. 17)
* B, C, Dare legitimate benefits of private LLMs.
* Ais incorrect - validation still requires time and resources.

NEW QUESTION # 213
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?

A. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
B. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.
C. Implementing a formal incident response plan to address incidents that may occur during system operation.
D. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.

Answer: A

Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remaininternal or shared with regulators.
From theAI Governance in Practice Report2025:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)

NEW QUESTION # 214
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