Which of the following factors is MOST important for preserving user confidence and trust in generative AI systems?
Answer(s): C
Users are more likely to trust generative AI systems when they clearly understand how their data is used, the system’s capabilities, and the associated risks. Transparent disclosure combined with informed consent ensures ethical use, builds confidence, and supports responsible AI deployment.
A retail organization implements an AI-driven recommendation system that utilizes customer purchase history. Which of the following is the BEST way for the organization to ensure privacy and comply with regulatory standards?
Answer(s): A
In a new supply chain management system, AI models used by participating parties are interactively connected to generate advice in support of management decision making. Which of the following is the GREATEST challenge related to this architecture?
Answer(s): D
When multiple AI models interact in a connected supply chain system, risks can propagate and compound across models. Assessing the overall, aggregate risk - including errors, biases, or failures from individual models - is challenging but critical for ensuring reliable and safe decision-making.
An organization uses an AI tool to scan social media for product reviews. Fraudulent social media accounts begin posting negative reviews attacking the organization's product. Which type of AI attack is MOST likely to have occurred?
Data poisoning occurs when attackers intentionally inject misleading or malicious data into the AI system’s training or input data. In this case, fraudulent social media accounts post negative reviews to manipulate the AI tool’s analysis, which is a classic example of data poisoning.
Which of the following BEST enables an organization to maintain visibility to its AI usage?
Answer(s): B
A centralized, detailed inventory provides clear visibility into which AI systems are in use, where they operate, and which business units rely on them. This enables effective governance, risk management, compliance oversight, and informed decision-making regarding AI deployment and monitoring.
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