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たくさんの人はISACA AAIA「ISACA Advanced in AI Audit」認証試験を通ることが難しいと思います。もし弊社の問題集を勉強してそれは簡単になります。弊社はオンラインサービスとアフターサービスとオンラインなどの全面方面を含めてます。オンラインサービスは研究資料模擬练習問題などで、アフターサービスはJpexamが最新の認定問題だけでなく、絶えずに問題集を更新しています。
質問 # 27
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?
正解：A
解説：
Cross-validation testing is a statistical method used to assess how well a model generalizes to an independent data set. The AAIATM Study Guide recommends this method as a best practice to fine-tune model accuracy and reduce both false positives and false negatives. It involves splitting the dataset into training and testing subsets multiple times to ensure model robustness.
"Cross-validation allows auditors and developers to identify overfitting and adjust model parameters to achieve better generalization and predictive accuracy, especially in fraud detection contexts."
質問 # 28
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card - Electrical Grid Predictive Maintenance Model
Model Information:
* Description: AI model designed to predict maintenance needs for electrical grid components, reduce unplanned downtime, and improve grid reliability.
* Inputs: Real-time sensor data, historical maintenance records, and operational logs.
* Outputs: Maintenance needs predictions for 60 & 90 days.Evaluation:
* Approach: Cross-validation and validation of accuracy, precision, and recall.
* Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%
正解：B
解説：
The F1 score is the harmonic mean of precision and recall, offering a balanced measure of model accuracy, especially when there is an imbalance in the classes (e.g., more "no-maintenance" than "needs-maintenance" outcomes). According to the AAIA™ Study Guide, the F1 score is used to evaluate how well the model identifies true positives while balancing the risk of false positives and false negatives.
"An F1 score summarizes the model's ability to correctly identify relevant events-in this case, true maintenance needs. A 76% F1 score means the model is relatively balanced and effective at catching maintenance requirements without generating too many false alerts." Thus, D is correct. Option A misrepresents recall as predictive accuracy. Option B misinterprets accuracy.
Option C has no direct basis in the excerpt.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Understanding Evaluation Metrics and Model Cards"
質問 # 29
An organization is evaluating whether to classify a new AI use case as "high-risk" under its internal AI risk framework. Which factor is MOST relevant to this determination?
正解：A
解説：
Risk classification frameworks (including the EU AI Act's approach) generally tier risk based on the potential impact of the AI system's decisions on individuals, not on technical implementation details.
質問 # 30
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?
正解：B
解説：
While regulatory compliance is essential, AAIA underlines thatethical integritymust guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is toensure ethical integrity throughout the AI life cycle(C) - from data collection and model design to deployment, monitoring, and retirement.
Option A focuses on privacy only, a subset of broader ethical considerations. Option B is valid but secondary; reputational protection is often a consequence of doing the right thing ethically. Option D (guardrails) is part of governance, but the overarching rationale for those guardrails is to uphold ethical principles. Therefore, comprehensive ethical stewardshipis the key driver.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (ethical AI principles, beyond compliance).
ISACA AI ethics and governance guidance emphasizing proactive, values-driven AI oversight.
質問 # 31
Which of the following is the GREATEST benefit of integrating AI in ransomware prevention?
正解：D
解説：
Ransomware often involves subtle, unusual patterns of data access and encryption activity. The primary advantage of AI is its ability to " Analyze large volumes of data in real-time " to identify behavioral anomalies that traditional signature-based security tools might miss. AI can detect " Zero-Day " ransomware by recognizing the intent of malicious actions (such as rapid file renaming or unauthorized encryption). This proactive detection is critical for stopping an attack before data exfiltration or total system lockout occurs.
While identifying attackers (Option C) or saving resources (Option D) are helpful, the core security value is the enhanced detection capability provided by AI-driven anomaly analysis.
質問 # 32
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