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Free PDF Quiz 2026 IAPP - AIGP - IAPP Certified Artificial Intelligence Governance Professional Exam Reference

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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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q129-Q134):

NEW QUESTION # 129
In the machine learning context, feature engineering is the process of?

A. Developing guidelines to train and test a model.
B. Converting raw data into clean data.
C. Creating learning schema for a model apply.
D. Extracting attributes and variables from raw data.

Answer: D

Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively. Reference: AIGP Body of Knowledge on AI Model Development and Feature Engineering.

NEW QUESTION # 130
You are an engineer that developed an AI-based ad recommendation tool. Which of the following should be monitored to evaluate the tool's effectiveness?

A. Input data, to ensure the ads are reaching the target audience.
B. Output data, to assess the delta between the prediction and actual ad clicks.
C. GPU performance, to evaluate the tool's robustness.
D. Algorithmic patterns, to show the model has a high degree of accuracy.

Answer: B

Explanation:
Monitoring output data and comparing predicted versus actual ad clicks provides a direct measure of the AI tool's effectiveness in recommending ads.

NEW QUESTION # 131
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. What business purpose it will serve.
B. How frequently it will be updated.
C. Whether its system card identifies risks.
D. Whether its training data is disclosed.

Answer: A

Explanation:
The primary consideration in selecting a proprietary AI model is whether it effectively serves the intended business purpose.

NEW QUESTION # 132
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of AI systems?

A. Department of Homeland Security (DHS).
B. National Science and Technology Council (NSTC).
C. National Institute of Standards and Technology (NIST).
D. Office of Science and Technology Policy (OSTP).

Answer: C

Explanation:
According to the White House Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (Executive Order 14110), the responsibility for creating guidelines to conduct red-teaming tests of AI systems falls to the National Institute of Standards and Technology (NIST). Specifically, the Executive Order directs NIST to establish guidelines and best practices for developing and deploying safe, secure, and trustworthy AI systems, including the creation of standards and procedures for developers to conduct AI red- teaming tests.

NEW QUESTION # 133
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)?

A. In case of breach of a provider's obligations for high-risk AI systems
B. In case of AI Act prohibitions
C. In case of the supply of misleading information to notified bodies in reply to a request
D. In case of a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies

Answer: A

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 # 134
......

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