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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 (Q153-Q158):

NEW QUESTION # 153
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. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
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. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.

Answer: D

Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remain internal or shared with regulators.
From theAI Governance in Practice Report 2024:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)

NEW QUESTION # 154
A shipping service based in the US is looking to expand its operations into the EU. It utilizes an in-house developed multimodal AI model that analyzes all personal data collected from shipping senders and recipients, and optimizes shipping routes and schedules based on this data.
As they expand into the EU, all of the following descriptions should be included in the technical documentation for their AI model EXCEPT?

A. A description of the prioritization of the risks of deployment of the AI system.
B. A description of the appropriateness of the performance metrics for the specific AI system.
C. A detailed description of the elements of the AI system and of the process for its development.
D. A general description of the AI system.

Answer: A

Explanation:
The EU AI Act outlines what must be included intechnical documentationfor high-risk systems. These requirements are designed to supportconformity assessment, transparency, and traceability.
From theAI Governance in Practice Report2025:
"It mandates drawing up technical documentation... must include a general description of the AI system, the intended purpose, and a detailed description of the elements and development process." (p. 34)
"Documentation... includes training, testing, evaluation procedures, andappropriateness of performance metrics." (p. 34-35) Therisk management systemis addressed separately through arisk management plan, not within the technical documentation itself.
Thus:
* A, C, and Dare explicitly required in thetechnical documentation.
* B, while important, is part of therisk management process, not a required section oftechnical documentation.

NEW QUESTION # 155
Training data is best defined as a subset of data that is used to:

A. Enable a model to detect and learn patterns.
B. Fine-tune a model to improve accuracy and prevent overfitting.
C. Resemble the structure and statistical properties of production data.
D. Detect the initial sources of biases to mitigate prior to deployment.

Answer: A

Explanation:
Training data is the dataset used to enable a machine learning model to detect and learn underlying patterns, which forms the basis for its predictive capabilities.

NEW QUESTION # 156
An autonomous vehicle maker deploys an AI system whereby the car is equipped with AI that makes decisions locally, without calling in to the AI's central server.
- The AI is capable of making decisions without waiting for a response
from a remote system.
- The AI can continue to function if it temporarily loses its
connection to the central server, such as driving through tunnels or
areas where its signal is lost.
- The AI can periodically receive updates to its model from its central server.
- A subset of anonymized data can be used to train the manufacturer's
AI model, depending on user privacy settings.
The manufacturer has deployed the AI using:

A. A multimodal model.
B. An edge model.
C. A cloud model.
D. A federated model.

Answer: B

Explanation:
An edge model processes data and makes decisions locally on the device, enabling operation without continuous connection to a central server.

NEW QUESTION # 157
CASE STUDY
A premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
To address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The organization continues planning the adoption of an AI tool to support hiring, but is concerned about potential bias in content generated by AI systems and how that could affect public perception.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?

A. Test the AI tool pre- and post-deployment
B. Continue to require the company's hiring personnel to manually screen all applicants
C. Require the procurement and deployment teams to agree upon the AI tool
D. Ensure the vendor provides indemnification for the AI tool

Answer: A

Explanation:
Note: This is the same scenario and question as Question 21 and thus has the same correct answer: A. It's possible this was duplicated in your original input.
Repeated for clarity:
"Testing AI tools pre- and post-deployment helps ensure they perform as expected and do not introduce bias, privacy issues, or fairness concerns. This mitigates reputational and legal risk." The AI Governance in Practice Report 2024 further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."

NEW QUESTION # 158
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