Which of the following is a common strategy for ongoing assurance of AI systems post-deployment?

Relying solely on user feedback for issue detection

Freezing the model to prevent any changes

Disabling monitoring after initial validation

Scheduled model retraining and performance reviews

A multinational corporation is adopting AI systems across its subsidiaries. The corporate governance office wants a consistent approach to AI assurance that can be adapted to different regulatory environments. Which framework would best provide a common language and structure for AI risk management across the organization?

ISO/IEC 42001, the AI management system standard.

The EU AI Act's requirements directly applied to all subsidiaries.

A custom internal checklist developed by one subsidiary.

The model's own documentation and test reports.

Which statement best defines AI assurance in the context of responsible AI governance?

The process of designing AI algorithms to maximize predictive accuracy on historical data.

The legal requirement that all AI systems must be audited by an external third party before deployment.

A set of activities and processes that provide evidence that an AI system meets specified requirements for reliability, safety, and compliance throughout its lifecycle.

A one-time certification that guarantees an AI system will never fail or produce harmful outcomes.

An auditor is reviewing an AI system used for hiring. The audit plan includes testing for bias, reviewing model documentation, and evaluating the system's compliance with relevant regulations. Which additional step would strengthen the audit's coverage of the system's lifecycle?

Request the system's source code for a manual code review.

Compare the system's accuracy to that of a human recruiter.

Review the system's monitoring and incident response procedures.

Interview the system's end users to gather anecdotal feedback.

EC-CouncilCertified Responsible AI Governance and Ethics

Domain 6Objective 2

AI Assurance, Testing, and Auditing CRAGE Practice Questions (Page 5)

Part of the AI Incident Response, Assurance, and Auditing domain, which makes up ~16% of our current practice bank.

53questions here

11free pages

8concepts

Questions 21–25

21foundation · easy
Which of the following is a common strategy for ongoing assurance of AI systems post-deployment?
Select an answer first
22foundation · easy
Which step in an AI audit involves defining the scope, objectives, and criteria against which the AI system will be evaluated?
Select an answer first
23application · medium
A multinational corporation is adopting AI systems across its subsidiaries. The corporate governance office wants a consistent approach to AI assurance that can be adapted to different regulatory environments. Which framework would best provide a common language and structure for AI risk management across the organization?
Select an answer first
24foundation · easy
Which statement best defines AI assurance in the context of responsible AI governance?
Select an answer first
25application · medium
An auditor is reviewing an AI system used for hiring. The audit plan includes testing for bias, reviewing model documentation, and evaluating the system's compliance with relevant regulations. Which additional step would strengthen the audit's coverage of the system's lifecycle?
Select an answer first

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AI Assurance, Testing, and Auditing CRAGE Practice Questions (Page 5)

Questions 21–25

Which of the following is a common strategy for ongoing assurance of AI systems post-deployment?

Which step in an AI audit involves defining the scope, objectives, and criteria against which the AI system will be evaluated?

Which statement best defines AI assurance in the context of responsible AI governance?

An auditor is reviewing an AI system used for hiring. The audit plan includes testing for bias, reviewing model documentation, and evaluating the system's compliance with relevant regulations. Which additional step would strengthen the audit's coverage of the system's lifecycle?

Keep studying — AI Incident Response, Assurance, and Auditing