AAIA考題套裝 & AAIA在線考題

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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Governance and Risk
Topic 2: AI Auditing Tools and Techniques
Topic 3: AI Operations

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最新的 Advanced in AI Audit AAIA 免費考試真題 (Q117-Q122):

問題 #117
What should be the IS auditor ' s GREATEST concern when an organization is mixing AI-generated content into training data without labeling?

A. The model propagates outdated information.
B. The model loses alignment safeguards.
C. The model outputs become inconsistent across similar queries.
D. The model reinforces its own mistakes as if they were facts.

答案：D

解題說明：
This is known as " Data Contamination " or " Model Autophagy " . When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as " ground truth, " which causes the model to " Collapse " and lose its ability to accurately reflect the real world. The ISACA AAIA™ manual emphasizes that training data must be
" Primary " and " Authentic " to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model ' s logic.

問題 #118
An AI social media platform uses an algorithm to increase user engagement that could unintentionally promote divisive content. Which of the following is the BEST course of action to mitigate this risk?

A. Suspend the algorithm until concerns are addressed.
B. Introduce controls allowing individuals to customize content preferences.
C. Obtain users' consent for the content they wish to view.
D. Regularly audit and adjust algorithms to reduce biases.

答案：D

問題 #119
An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?

A. Portability of data
B. Retention of stored data
C. Accountability for data management
D. Data practices for training models

答案：C

解題說明：
Accountability for data management (option D) is the most crucial consideration. The AAIATM Study Guide emphasizes that "clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls." Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.

問題 #120
During an audit of a bank ' s AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?

A. Prepare an audit report.
B. Conduct an explainability assessment.
C. Evaluate transparency controls.
D. Evaluate appeal processes.

答案：C

解題說明：
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision-making. If an auditor finds that applicants were not informed, the immediate " First " step is to " Evaluate transparency controls " to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).

問題 #121
Which of the following presents the MOST significant barrier to generative AI model explainability?

A. Insufficient staff experience with generative AI tools
B. Lack of alignment between stakeholder groups
C. Bias within data sets used for model training
D. Rapid evolution of algorithm capabilities

答案：D

解題說明：
The rapid evolution of modern generative AI architectures (option B) is the largest barrier to explainability.
Complex deep learning models like LLMs, diffusion models, and transformer-based architectures involve millions or billions of parameters, making it extremely challenging to determine precisely how outputs are produced.
AAIA notes that explainability challenges arise because:
Model structures are highly complex
Parameter interactions are nonlinear
Internal representations are not human-interpretable
Continuous updates make documentation outdated
Training data and latent representations create opaque reasoning chains

問題 #122
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