AIGP Actual Questions, AIGP Valid Exam Fee

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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 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 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 (Q143-Q148):

NEW QUESTION # 143
All of the following may be permissible uses of an AI system under the EU AI Act EXCEPT:

A. To implement social scoring.
B. To promote equitable distribution of welfare benefits.
C. To detect an individual's intent for law enforcement purposes.
D. To manage border control.

Answer: A

Explanation:
The EU AI Act explicitly prohibits AI systems used for social scoring due to the significant risks to fundamental rights.

NEW QUESTION # 144
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.
All of the following are potential negative consequences created by using the AI tool to help make hiring decisions EXCEPT?

A. Privacy violations
B. Candidate quality
C. Disparate impacts
D. Automation bias

Answer: B

Explanation:
The correct answer isB. "Candidate quality" isnota negative consequence of using AI-rather, it is the intendedbenefitof using such tools (e.g., more efficient filtering of strong candidates).
From the AIGP ILT Guide:
"Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring.
These risks may arise when AI models replicate biases present in training data or obscure the decision logic." AI Governance in Practice Report2025(Bias and Fairness Section) also warns:
"Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups." Candidate quality is agoal, not a risk, makingB the correct answerfor what isnota negative outcome.

NEW QUESTION # 145
What is the technique to remove the effects of improperly used data from an ML system?

A. Model disgorgement.
B. Data cleansing.
C. Data de-duplication.
D. Model inversion.

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 # 146
Which of the following steps occurs in the design phase of the AI life cycle?

A. Model explainability.
B. Impact assessment.
C. Performance evaluation.
D. Data augmentation.

Answer: B

Explanation:
Conducting an impact assessment is a key step during the design phase to anticipate and address potential risks and ethical considerations before development proceeds.

NEW QUESTION # 147
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 best exemplifies human oversight capabilities you should enable under the relevant AI laws?

A. The company has established an AI governance team within each of its departments who are specifically required to evaluate the performance metrics, including anomalies, to ensure the AI tool is operating correctly.
B. The tool manual requires that all physicians are required to undergo specific training regarding how to interpret the AI ' s output, and understand its limitations.
C. The tool requires a medical doctor to approve a diagnosis before the diagnosis is entered into the patient
' s medical record.
D. The agentic tool that helps schedule appointments and refill prescriptions leverages Retrieval- Augmented Generation RAG to confirm there are no medication contraindications before refilling.

Answer: C

Explanation:
The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act.
Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize "human-in-the-loop" controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.

NEW QUESTION # 148
......

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