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| Section | Weight | Objectives |
|---|---|---|
| Foundational Concepts of AI Governance | 24–28% | - Principles of responsible, ethical and trustworthy AI - Governance frameworks and core concepts - Fundamental AI definitions, types and characteristics - Impacts, risks and benefits of AI systems |
| Laws, Regulations, Standards and Frameworks | 24–28% | - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations - Global and regional regulations (EU AI Act, US, etc.) - Alignment with data protection and privacy laws |
| Governance of AI Deployment and Use | 21–25% | - Lifecycle maintenance, decommissioning and updates - Incident response, accountability and audit - Selection, procurement and third-party management - Implementation, monitoring and ongoing oversight |
| Governance of AI Development | 21–25% | - Testing, validation, documentation and transparency - Model management, version control and quality assurance - Governance through design and lifecycle management - Requirements gathering, risk assessment and mitigation |
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NEW QUESTION # 190
CASE STUDY
Please use the following answer the next question:
XYZ Corp., 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.
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 are responsible for integrating and deploying 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.
If XYZ does not deploy and use the Al hiring tool responsibly in the United States, its liability would likely increase under all of the following laws EXCEPT?
Answer: C
Explanation:
In the United States, the use of AI hiring tools must comply with anti-discrimination laws, accessibility laws, and privacy laws to avoid increasing liability. Anti-discrimination laws (A) ensure that hiring practices do not unlawfully discriminate against protected classes. Accessibility laws (C) require that hiring tools are accessible to all applicants, including those with disabilities. Privacy laws (D) govern the handling of personal data during the hiring process. Product liability laws (B), however, typically apply to the safety and reliability of physical products and would not generally increase liability specifically related to the responsible use of AI hiring tools in the employment context.
NEW QUESTION # 191
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: B
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 # 192
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?
Answer: B
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 # 193
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?
Answer: A
Explanation:
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report 2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data ' s lawful collection by the databroker..." (p. 19) This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.
NEW QUESTION # 194
A company initially intended to use a large data set containing personal information to train an AI model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model. This is an example of applying which privacy-enhancing technique (PET)?
Answer: A
Explanation:
Anonymization permanently removes personal identifiers from data, preventing re-identification and enhancing privacy.
NEW QUESTION # 195
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