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| Section | Objectives |
|---|---|
| Topic 1: AI Governance Implementation | - Organizational Governance
|
| Topic 2: AI Risk Management | - Operationalizing Risk Management
|
| Topic 3: Foundations of AI Governance | - Impacts of AI
|
| Topic 4: AI Laws, Regulations, and Standards | - Regional and National Regulations
|
| Topic 5: AI Governance Management | - Emerging Issues
|
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NEW QUESTION # 216
Which of the following is an example of a high-risk application under the EU AI Act?
Answer: B
NEW QUESTION # 217
Which of the following AI uses is best described as human-centric?
Answer: D
Explanation:
Virtual assistants that personalize education based on individual abilities and needs focus directly on enhancing human learning experiences, making this use human-centric.
NEW QUESTION # 218
Which of the following best describes the data minimization principle as it relates to an AI model?
Answer: A
Explanation:
A best reflects practical application of data minimization to machine learning. Data minimization requires personal data to be adequate, relevant, and limited to what is necessary for the specified purpose. AI development does not categorically prohibit using personal data in training, so B is too absolute. Personal data may legitimately be required during both training and inference, making D incorrect. Manual review of every data item is neither the definition nor a necessary implementation of minimization. Privacy-enhancing techniques can instead reduce the amount or identifiability of personal data processed while retaining model utility. ICO AI guidance specifically identifies perturbation or adding noise, synthetic data, and federated learning as techniques that can support data minimization during model training. Thus, A is the technically and governance-aligned choice.
NEW QUESTION # 219
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria such as a confidence score below a threshold.
To date, the healthcare network has:
Defined its AI ethical principles
Conducted discovery to identify the intended uses and success criteria for the system Established an AI risk committee Assembled a cross-functional team with clear roles and responsibilities Created policies and procedures to document standards, workflows, timelines and risk thresholds during the project The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network ' s existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the AI solution?
Answer: A
Explanation:
The correct answer is C because performing an impact assessment is the most effective proactive measure to identify and mitigate discrimination risks before model training and testing. AI governance frameworks emphasize early-stage risk identification, particularly for high-stakes domains like healthcare, where bias can lead to harmful or inequitable outcomes. Impact assessments, such as algorithmic or data protection impact assessments, evaluate potential harms, affected populations, and fairness risks prior to development. This enables organizations to design appropriate safeguards, adjust data selection, and implement mitigation strategies before biases become embedded in the model. Options like audits and bias bounty programs are reactive or post-development controls, while simply acquiring more data does not guarantee reduced bias without proper analysis. A structured impact assessment aligns with risk-based governance and supports fairness, accountability, and regulatory compliance.
NEW QUESTION # 220
When assessing the success of an AI system in meeting its objectives, which of the following approaches best aligns with the requirement to ensure a comprehensive evaluation while avoiding automation bias?
Answer: A
NEW QUESTION # 221
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