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| Section | Objectives |
|---|---|
| Foundations of AI Systems and Governance | - Governance principles and accountability
|
| Regulatory and Legal Frameworks for AI | - Global AI governance regulations
|
| AI Risk Management and Impact Assessment | - Risk identification and mitigation
|
| AI Lifecycle Governance Implementation | - Operational governance controls
|
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NEW QUESTION # 48
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model ' s output?
Answer: C
Explanation:
The correct answer is C because differential privacy is specifically designed to ensure that the inclusion or exclusion of any single data point does not significantly affect the output of a model. This provides strong mathematical guarantees that individual training data cannot be reverse-engineered or identified from model outputs. Differential privacy works by introducing controlled noise into the data or model outputs, thereby protecting individual-level information while preserving overall statistical patterns. In AI governance, it is widely recognized as a key privacy-enhancing technology for mitigating risks such as data leakage, membership inference attacks, and re-identification. Other options, such as homomorphic encryption, focus on secure computation, while data sharding and compartmentalization address data management but do not provide formal privacy guarantees at the model output level.
NEW QUESTION # 49
What is the best reason for a company to adopt a policy that prohibits the use of generative AI?
Answer: D
Explanation:
The correct answer is D because one of the most significant risks associated with generative AI is the potential leakage of sensitive, confidential, or proprietary information. AI governance frameworks emphasize data protection, confidentiality, and secure data handling as critical organizational responsibilities. When employees input sensitive data into generative AI tools, especially external or third-party systems, that information may be stored, reused, or exposed unintentionally. This creates legal, regulatory, and reputational risks. As a result, organizations may restrict or prohibit generative AI use to prevent unauthorized data disclosure. The other options relate to operational or business considerations, which are not primary governance drivers. Protecting sensitive information aligns with core AI governance principles such as privacy, security, and risk mitigation, making it the strongest justification for such a policy.
NEW QUESTION # 50
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT:
Answer: A
Explanation:
Autoregression is a statistical modeling technique for time-series analysis, not a weight optimization technique used in deep learning neural networks.
NEW QUESTION # 51
The processes and methods that allow human users to understand and trust the outputs produced by AI are important in addressing which key regulatory concern?
Answer: D
Explanation:
Explainable AI focuses specifically on providing users with understandable reasoning behind AI outputs so they can interpret, evaluate, and trust the system's decisions.
NEW QUESTION # 52
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely 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 a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and 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 and a consulting firm to 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.
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is?
Answer: C
Explanation:
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is privacy risk. Combining data sets, especially in healthcare, often involves handling sensitive information that could lead to privacy breaches if not managed properly. De-identified data can still pose re-identification risks when combined with other data sets. Ensuring privacy involves implementing robust data protection measures, maintaining compliance with privacy regulations such as HIPAA, and conducting thorough privacy impact assessments. Reference: AIGP Body of Knowledge on Data Privacy and Security.
NEW QUESTION # 53
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