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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 (Q80-Q85):

NEW QUESTION # 80
A US hospital plans to develop an AI that will review available patient data in order to propose an initial diagnosis to licensed physicians. The hospital will implement a policy that requires physicians to consider the AI proposal, but conduct their own physical examinations prior to making a final diagnosis.
An important ethical concern with this plan is?

A. Whether the AI was trained on a representative dataset.
B. Whether the AI will have an error rate comparable to human physicians.
C. Whether physicians understand how the AI works.
D. Whether patients will receive an economic benefit from the use of AI.

Answer: A

Explanation:
The core ethical concern when deploying diagnostic AI in a healthcare setting is ensuringfairness and accuracy across diverse patient populations. If the AI is trained on a dataset that isnot representativeof the population it will serve, it risks reinforcing health disparities and leading to misdiagnoses.
From theAI Governance in Practice Report2025:
"Training datasets lacking in diversity can produce outputs that systematically underperform for certain groups... this can lead to inaccurate or biased outcomes in healthcare settings." (p. 41)
"Bias, discrimination and fairness challenge... inadequate or nonrepresentative training data can result in AI systems that propagate historical disparities." (p. 42) While physician oversight may reduce risk,biased data can still shape clinical decision-making.
* A- Economic benefit is not central to ethical risk here.
* C- Important but less critical than data representativeness.
* D- Error rate matters but is addressed via validation; it's not the core ethical issue.

NEW QUESTION # 81
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
B. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
C. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.
D. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.

Answer: C

Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals.
Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

NEW QUESTION # 82
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

A. Generative Al.
B. Expert systems.
C. Segmentation.
D. Supervised learning.

Answer: D

Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference:
AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).

NEW QUESTION # 83
What is the best method to proactively train an LLM so that there is mathematical proof that no specific piece of training data has more than a negligible effect on the model or its output?

A. Data compartmentalization.
B. Clustering.
C. Transfer learning.
D. Differential privacy.

Answer: D

Explanation:
Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.
Reference: AIGP BODY OF KNOWLEDGE, pages related to data privacy and security in model training.

NEW QUESTION # 84
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?

A. Business A should fine-tune the AI model on user-generated text that has been verified to be appropriate.
B. Business A should ask Business B for detailed documentation on the generative AI model's training data and whether it contained toxic or obscene sources.
C. Business A should establish a user reporting feature that allows users to flag toxic or obscene text, and report any incidents to Business B.
D. Business A should test that the AI model performs as expected and meets their minimum requirements for filtering toxic or obscene text.

Answer: D

Explanation:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.

NEW QUESTION # 85
......

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