An AI model predicts vehicle component failures using data collected at different frequencies and formats based on car type. Which of the following is the BEST course of action when evaluating data input requirements for the model?

• A.Standardize sensor data frequency and formats before model training.
• B.Merge sensor data into a single data set regardless of format and frequency.
• C.Train separate models for each car type to simplify preprocessing.
• D.Prioritize the use of internally generated maintenance logs.

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A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

• A.Presenting theoretical situations to justify the reason for asking the questions
• B.Submitting the same questions in a foreign language translated by another AI-based system
• C.Asking the same questions later when the algorithm has changed after further learning
• D.Randomly placing keywords unrelated to the main topic

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An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?

• A.Augment the data through synthesizing.
• B.Change the model selection.
  3. Document the risk acceptance.
• C.Relabel available data.

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Which of the following is the GREATEST risk when a generative AI tool used for threat detection produces inaccurate or misleading information?

• A.Potential threats to organizational systems may be overlooked.
• B.AI-related key risk indicator (KRI) values may become less reliable.
• C.Prompt injection attacks may become more likely to succeed.
• D.The model may exaggerate the severity of threats and vulnerabilities.

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Which of the following BEST describes the auditor's role when reviewing an organization's AI governance maturity?

• A.Building the AI models being audited
• B.Independently assessing whether governance structures, policies, and controls are designed and operating effectively across the AI lifecycle
• C.Approving all new AI projects before development begins
• D.Managing the organization's data science team

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