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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance of AI Development | 21–25% | - Model management, version control and quality assurance - Requirements gathering, risk assessment and mitigation - Governance through design and lifecycle management - Testing, validation, documentation and transparency |
| Topic 2: Governance of AI Deployment and Use | 21–25% | - Incident response, accountability and audit - Selection, procurement and third-party management - Lifecycle maintenance, decommissioning and updates - Implementation, monitoring and ongoing oversight |
| Topic 3: Foundational Concepts of AI Governance | 24–28% | - Fundamental AI definitions, types and characteristics - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts - Principles of responsible, ethical and trustworthy AI |
| Topic 4: Laws, Regulations, Standards and Frameworks | 24–28% | - Sector-specific requirements and compliance obligations - International standards (ISO 42001, NIST AI RMF, etc.) - Alignment with data protection and privacy laws - Global and regional regulations (EU AI Act, US, etc.) |
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NEW QUESTION # 28
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?
Answer: B
Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).
NEW QUESTION # 29
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?
Answer: D
Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model. Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.
NEW QUESTION # 30
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?
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.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.
NEW QUESTION # 31
Which of the following best defines an "AI model"?
Answer: B
Explanation:
An AI model is a program trained on data to identify patterns and make predictions or decisions based on that training.
NEW QUESTION # 32
Scenario:
A company using AI for resume screening understands the risks of algorithmic bias and the evolving legal requirements across jurisdictions. It wants to implement the right governance controls to prevent reputational damage from misuse of the AI hiring tool.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
Answer: C
Explanation:
The correct answer isA. Pre- and post-deployment testing ensuresbias, accuracy, and fairnessare evaluated and corrected as needed, which isessential for reputational risk mitigation.
From the AIGP Body of Knowledge:
"Testing AI systems before and after deployment is critical to ensure performance, fairness, and compliance.
Failing to do so may result in reputational damage and legal exposure." AI Governance in Practice Report2025(Bias/Fairness and Risk Sections):
"System impact assessments, testing, and post-deployment monitoring are necessary to identify and mitigate risks... This supports both compliance and public trust." Testing is proactive, unlike indemnification (which transfers risk after damage), or requiring manual review (which defeats automation).
NEW QUESTION # 33
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