Which of the following is the MOST important AI data governance method to protect privacy and data security?
Answer(s): D
Periodic monitoring is essential for detecting privacy violations, unauthorized data use, and emerging security risks in AI systems. Continuous oversight ensures that data handling practices remain compliant and secure throughout the AI life cycle, making it the most important governance method for protecting privacy and data security.
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
Dynamic pricing based on customer profiles carries a high risk of discriminatory outcomes. Testing must ensure the AI does not unintentionally produce biased or unfair pricing decisions, making this the most important governance and ethical concern during development.
Which of the following should be done FIRST when developing an incident management process for AI threats?
Answer(s): B
Defining clear roles and responsibilities is the foundational first step in developing an AI incident management process. Without clarity on who is accountable for detection, response, decision-making, and communication, subsequent procedures such as classification and escalation cannot be executed effectively.
Which of the following roles is BEST suited to define the implementation roadmaps for adopting AI solutions?
A steering committee is responsible for strategic oversight and is best positioned to define AI implementation roadmaps, ensuring alignment with organizational goals, governance requirements, and enterprise-wide priorities.
After AI training data has been tested for biases, which of the following is MOST important to check in order to validate the effectiveness of the testing?
After bias testing, the most important validation step is ensuring that the AI model’s outputs stay within the organization’s acceptable risk level. This confirms that bias mitigation efforts were effective and that resulting decisions do not create unacceptable ethical, legal, or operational risks.
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