最實用的AAIA認證考古試題及參考答案

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2026 KaoGuTi最新的AAIA PDF版考試題庫和AAIA考試問題和答案免費分享：https://drive.google.com/open?id=1fTDNs48UqzuJM8oGCu3MEuUF4HTnzwdD

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ISACA AAIA 考試大綱：

主題簡介
主題 1
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
主題 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
主題 3
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.

>> AAIA資訊 <<

AAIA在線題庫 - AAIA套裝

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最新的 Advanced in AI Audit AAIA 免費考試真題 (Q167-Q172):

問題 #167
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?

答案：D

解題說明：
Generative AI systems, particularly those based on transformer models, produce outputs using probabilistic computations. As a result, even when given the same input data, these models may generate different outputs depending on sampling strategies (e.g., temperature, top-k sampling).
"Generative AI operates probabilistically, meaning that outputs can vary with each run based on stochastic sampling techniques. This variability is expected and must be accounted for in risk-sensitive environments like finance." While A and B refer to limitations and architecture, and D is unrelated to logic, C directly explains the output inconsistency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Stochastic Behavior in Generative Models"


問題 #168
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

答案：A


問題 #169
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

答案：D

解題說明：
In predictive maintenance use cases-such as detecting turbine failure-the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA™ Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Evaluation Metrics and Predictive Accuracy"


問題 #170
An IS auditor has discovered that an organization ' s AI system is less accurate than the required threshold and recommends management implement cross-referencing multiple models in order to produce more accurate results. This is an example of which type of risk response?

答案：C

解題說明：
Risk mitigation involves implementing controls or actions to reduce the likelihood or impact of a risk to an acceptable level. In this scenario, the model ' s failure to meet accuracy thresholds poses a performance and operational risk. By recommending the use of model ensembles or cross-referencing (where multiple models evaluate the same input), the auditor is proposing a technical control designed to improve the reliability of the system ' s output. This reduces the risk of incorrect decisions without eliminating the system entirely (Avoidance) or moving the risk to a third party (Transfer). Mitigation is the standard response for improving AI performance while maintaining the existing use case.


問題 #171
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?

答案：C

解題說明：
Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIA™ Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.
"Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black-box models." While documentation (A) and reporting (C) are part of governance, D directly supports decision-level clarity.
Dashboards (B) show results, not rationale.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Model Explainability and Regulatory Assurance"


問題 #172
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AAIA在線題庫: https://www.kaoguti.com/AAIA_exam-pdf.html

2026 KaoGuTi最新的AAIA PDF版考試題庫和AAIA考試問題和答案免費分享：https://drive.google.com/open?id=1fTDNs48UqzuJM8oGCu3MEuUF4HTnzwdD

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