Professional IAPP - AIGP Study Guide Pdf

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
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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2026 AIGP Study Guide Pdf | Latest IAPP AIGP: IAPP Certified Artificial Intelligence Governance Professional 100% Pass

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q73-Q78):

NEW QUESTION # 73
CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
All of the following should be included in the marketing company's disclosures about the use of the LLM EXCEPT:

Answer: C

Explanation:
Disclosures by the marketing company should include the intended purpose, legal compliance, and limitations of the LLM but do not typically require revealing proprietary methods, which are confidential.


NEW QUESTION # 74
Machine learning is best described as a type of algorithm by which?

Answer: A

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.


NEW QUESTION # 75
Retraining an LLM can be necessary for all of the following reasons EXCEPT?

Answer: C

Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.


NEW QUESTION # 76
Scenario:
A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act.
The regulator is considering penalties under the enforcement provisions of the regulation.
According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to
€15 million or 3% of annual worldwide turnover(whichever is higher)?

Answer: D

Explanation:
The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of €15 million or 3% of annual global turnover.
From the AIGP ILT Guide - EU AI Act Module:
"Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to €15 million or 3% of turnover." AI Governance in Practice Report2025supports this:
"Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article
71(3) of the EU AI Act."
Note: Thehighest penalty (€35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.


NEW QUESTION # 77
Scenario:
An organization is planning to deploy a new internal application that uses AI to make automated decisions about individuals. This application will process personal information and may affect individuals' access to certain benefits or opportunities.
Which of the following documents must be updated to ensure transparency?

Answer: D

Explanation:
The correct answer is D. Transparency obligations under data protection laws, such as GDPR and most AI governance frameworks, require that users whose data is being processed be directly informed.
From the AIGP ILT Guide (Privacy Module):
"The user privacy notice must be updated to explain the nature of automated processing, the logic involved, and the significance and consequences for the data subject." Also, per AI Governance in Practice Report 2024 (Part III):
"Transparency obligations apply throughout the lifecycle of AI... Individuals must be informed about automated decision-making and profiling that may impact them." Unlike internal policies or general privacy notices, the user privacy notice provides direct transparency to the individual data subjects affected by AI processing.


NEW QUESTION # 78
......

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