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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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 (Q38-Q43):

NEW QUESTION # 38
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company ' s core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following would provide you the best information addressing quality principles pertaining to the functioning of the AI agents and LLM?

Answer: C

Explanation:
The correct answer is D because it directly reflects core data and model quality principles such as accuracy, performance consistency, and real-world effectiveness across different user groups. AI governance frameworks emphasize that quality must be evaluated based on whether outputs are accurate, complete, and fit for purpose in real-world conditions. Measuring accuracy by user group also supports fairness and bias detection, which are essential components of trustworthy AI. Option D captures outcome-based performance and aligns with continuous monitoring expectations across the AI lifecycle. In contrast, options A and C focus more on operational or technical metrics, while B reflects user sentiment rather than objective quality.
According to AI governance principles, high-quality AI systems require ongoing evaluation of outputs against real-world results to ensure reliability, validity, and safe deployment.


NEW QUESTION # 39
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:

Answer: B

Explanation:
Ensuring the AI is trained on representative data is crucial to avoid biased or inaccurate diagnostic proposals that could negatively impact patient care.


NEW QUESTION # 40
A French medical research center wishes to develop an AI-based system that will predict the risk of serious diseases based on patients ' genetic data. To do so, it contracts with a technology company and provides it with patients ' data previously obtained by the center during research.
To guarantee compliance when processing special categories of personal data, the medical research center must ensure that?

Answer: A

Explanation:
Genetic and health information are special categories of personal data under Article 9 of the GDPR, for which processing is generally prohibited unless a valid Article 9 exception applies. Among the available answers, explicit consent provides the clearest applicable condition: Article 9(2)(a) permits processing where the data subject has explicitly consented to processing for specified purposes. Importantly, explicit consent is not the only possible legal condition under the GDPR; scientific research and healthcare processing may sometimes rely on other Article 9 conditions where applicable law and safeguards permit. However, A merely describing a preventive-medicine purpose does not itself establish the necessary legal condition. D is irrelevant because EU establishment and non-cloud processing do not establish lawfulness. B would materially change the analysis only if the data were genuinely anonymized so that they were no longer personal data.


NEW QUESTION # 41
What is the technique to remove the effects of improperly used data from an ML system?

Answer: A

Explanation:
Model disgorgement is the technique used to remove the effects of improperly used data from an ML system.
This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.


NEW QUESTION # 42
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?

Answer: C

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 # 43
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