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IAPP AIGP Exam Syllabus Topics:

Section	Objectives
AI Laws, Regulations, and Standards	- Regional and National Regulations 1. EU AI Act 2. National AI Strategies 3. US State and Federal Regulations - Global AI Frameworks 1. UNESCO AI Ethics 2. OECD AI Principles 3. ISO/IEC Standards
AI Governance Implementation	- Data Governance for AI 1. Data Quality and Integrity 2. Data Privacy and Protection - Organizational Governance 1. Roles and Responsibilities (RACI) 2. Policies and Procedures 3. Governance Structures
AI Governance Management	- Life Cycle Management 1. Auditing and Monitoring 2. Third-party management 3. Incident Management - Emerging Issues 1. Future Trends 2. Ethical AI 3. Generative AI
AI Risk Management	- Risk Frameworks and Standards 1. NIST AI Risk Management Framework (RMF) 2. ISO 31000 Risk Management - Operationalizing Risk Management 1. Risk mitigation strategies 2. Risk identification and assessment
Foundations of AI Governance	- Impacts of AI 1. Benefits of AI 2. Risks of AI - AI Governance Fundamentals 1. Key AI Terminology and Concepts 2. Understanding the AI Development Life Cycle 3. Defining AI and its different approaches

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which of the following is NOT a common type of machine learning?

A. Reinforcement learning.
B. Deep learning.
C. Unsupervised learning.
D. Cognitive learning.

Answer: D

Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.

NEW QUESTION # 39
You are an engineer that developed an Al-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. GPU performance, to evaluate the tool's robustness.
C. Algorithmic patterns, to show the model has a high degree of accuracy.
D. Output data, assess the delta between the prediction and actual ad clicks.

Answer: D

Explanation:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.

NEW QUESTION # 40
Decreasing the complexity of a machine learning model reduces variance and:

A. Decreases tolerance.
B. Decreases interpretability.
C. Increases bias.
D. Increases accuracy.

Answer: C

Explanation:
Reducing model complexity lowers variance but typically increases bias, reflecting a trade-off between underfitting and overfitting.

NEW QUESTION # 41
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 the supply of misleading information to notified bodies in reply to a request
B. In case of breach of a provider's obligations for high-risk AI systems
C. In case of a breach of AI Act prohibition by the Union institutions, bodies, offices and agencies
D. In case of AI Act prohibitions

Answer: B

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 Report 2024 supports 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 # 42
The most important policy to assess the operations of an AI model is to follow the:

A. Code of conduct policy of the model provider.
B. Privacy policy of the model provider.
C. Security policy of the model provider.
D. Acceptable use policy of the model provider.

Answer: D

Explanation:
The acceptable use policy defines how the model is permitted to be operated, including restrictions, prohibited behaviors, and required safeguards. It is the most important policy for assessing whether the AI model is being used appropriately and within operational boundaries.

NEW QUESTION # 43
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