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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 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 (Q193-Q198):

NEW QUESTION # 193
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. Input data, to ensure the ads are reaching the target audience.
B. Output data, to assess the delta between the prediction and actual ad clicks.
C. Algorithmic patterns, to show the model has a high degree of accuracy.
D. GPU performance, to evaluate the tool ' s robustness.

Answer: B

Explanation:
The correct answer is A because evaluating the effectiveness of an AI system requires measuring how well its outputs align with real-world outcomes. In this case, comparing predicted ad engagement with actual user clicks provides a direct measure of model performance and business impact. AI governance frameworks emphasize outcome-based monitoring to ensure systems meet their intended objectives and deliver value.
Tracking output performance also supports continuous improvement by identifying gaps between predictions and reality. Options B and C focus on indirect indicators, such as internal model behavior or input targeting, which do not fully capture effectiveness. Option D relates to infrastructure performance rather than model outcomes. Monitoring output accuracy and real-world results is essential for validating AI system effectiveness and ensuring accountability.

NEW QUESTION # 194
A Canadian company is developing an Al solution to evaluate candidates in the course of job interviews.
Before offering the Al solution in the EU market, the company must take all of the following steps EXCEPT?

A. Register the Al solution in a public EU database.
B. Draw up technical documentation and instructions for use.
C. Engage a third-party auditor to perform a bias audit.
D. Establish a risk and quality management system.

Answer: A

Explanation:
Before offering an AI solution in the EU market, a Canadian company must take several steps to comply with the EU AI Act. These steps include establishing a risk and quality management system (B), engaging a third-party auditor to perform a bias audit (C), and drawing up technical documentation and instructions for use (D). However, there is no requirement to register the AI solution in a public EU database (A). This registration step is not specified as part of the compliance requirements under the EU AI Act for such solutions.

NEW QUESTION # 195
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?

A. The number of stakeholders.
B. The complexity of the AI model.
C. The quality of the training data.
D. The volume of training data.
E. The maturity of AI governance.

Answer: B,C,D

Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report 2025:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32) B . Maturity of governanceaffects oversight, not training time.
D . Number of stakeholdersaffects alignment, not direct training duration.

NEW QUESTION # 196
Which of the following use cases would be best served by a non-AI solution?

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

Answer: A

NEW QUESTION # 197
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. What business purpose it will serve.
B. Whether its training data is disclosed.
C. Whether its system card identifies risks.
D. How frequently it will be updated.

Answer: A

Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report2025:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

NEW QUESTION # 198
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