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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Management | 31% | - AI vendor and supply chain risk management - Regulatory compliance and ethical considerations in AI - AI risk assessment, thresholds, and treatment strategies - AI threat and vulnerability management |
| AI Technologies and Controls | 38% | - Security monitoring, testing, and continuous assurance for AI systems - Privacy, ethics, trust, and safety controls - AI lifecycle security: model selection, training, validation, and deployment - AI security architecture and secure design principles - Data management and protection controls for AI |
| AI Governance and Program Management | 31% | - AI governance frameworks and alignment with business objectives - AI security program development and management - AI security policies, standards, and procedures - AI asset and data lifecycle management - Business continuity and incident response for AI systems |
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NEW QUESTION # 421
An AI-powered drone surveillance system is deployed at a construction site. Which of the following is the MOST appropriate control to detect adversarial threats in the visual recognition model?
Answer: A
Explanation:
Adversarial threats in visual recognition models involve manipulated inputs designed to deceive the model. Testing the model's robustness against image manipulation directly evaluates its ability to detect and resist such adversarial inputs, making it the most appropriate control.
NEW QUESTION # 422
Which of the following would BEST help to prevent attackers from exploiting an AI system to infer personal data?
Answer: A
Explanation:
Restricting the number of queries per user helps reduce the likelihood of inference attacks, where attackers repeatedly query an AI system to extract or reconstruct sensitive personal information from the model's responses. Limiting query volume directly constrains the attacker's ability to gather enough information for successful inference.
NEW QUESTION # 423
An AI fraud detection system frequently flags legitimate transactions as suspicious, leading to customer dissatisfaction and operational obstacles. Which of the following is the BEST way to mitigate this issue without compromising security?
Answer: D
Explanation:
Conducting a model review helps identify why legitimate transactions are being incorrectly flagged and enables improvements to model accuracy. Adding explainability techniques also helps analysts and stakeholders better understand decision outcomes, reducing operational issues while maintaining effective fraud detection capabilities.
NEW QUESTION # 424
Which of the following is the MOST effective way to identify deepfakes generated by AI?
Answer: D
Explanation:
Analyzing biometric inconsistencies is the most effective method because deepfakes often fail to perfectly replicate natural human physiological and behavioral characteristics such as facial movements, blinking patterns, voice characteristics, or lip synchronization. These inconsistencies provide strong indicators of AI-generated manipulation.
NEW QUESTION # 425
Which of the following control strategies BEST mitigates the risk of user harm from a large language model (LLM) generating offensive or biased content?
Answer: D
Explanation:
Output filtering and moderation directly address the risk of harmful, offensive, or biased content by reviewing and controlling generated responses before they reach users. This helps enforce safety, ethical, and compliance standards in LLM outputs.
NEW QUESTION # 426
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