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ISACA AAIA 認定試験の出題範囲:
| トピック | 出題範囲 |
|---|
| トピック 1 | - 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.
|
| トピック 2 | - 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.
|
| トピック 3 | - 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.
|
>> AAIA資格問題集 <<
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ISACA Advanced in AI Audit 認定 AAIA 試験問題 (Q49-Q54):
質問 # 49
An internal audit department notices that AI-generated audit reports are producing false conclusions. Which of the following is the BEST way to correct this issue?
- A. Decrease the model's creativity score.
- B. Increase the model context.
- C. Update service level agreements (SLAs).
- D. Suspend utilization of the tool until resolved.
正解:D
解説:
If AI-generated audit reports contain false conclusions, the integrity of audit evidence and overall assurance is compromised. According to AAIA, when AI systems compromise accuracy of audit outputs, the immediate step should be to suspend use (B) to prevent further incorrect or misleading reporting.
This allows auditors to:
Investigate the root cause of incorrect conclusions
Validate the model's training data, rules, and prompt structures
Reassess controls, safeguards, and human review processes
Prevent reliance on unreliable AI-generated audit material
質問 # 50
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?
- A. The hyperparameters are not optimized.
- B. Consent is not properly managed.
- C. Data versioning controls were not developed.
- D. Compute scale training was not performed.
正解:A
解説:
False positives in fraud detection AI systems often stem from poorly optimized hyperparameters.
Hyperparameters control aspects of the model's learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.
"Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition."
質問 # 51
An organization shares an AI model with external partners. One partner reports that sensitive data has been inadvertently exposed through the model's outputs. Which of the following is the IS auditor's BEST recommendation?
- A. Disable the shared model and notify partners of the potential breach.
- B. Retrain the model immediately and implement privacy-preserving techniques.
- C. Audit the data pipelines of all partners to identify the source of the leak.
- D. Limit the model's outputs to anonymized results while investigating further.
正解:A
解説:
In the case of a potential data exposure through AI model outputs, the first and most responsible action from an auditing and risk standpoint is to halt further risk propagation. According to the AAIA™ Study Guide, immediate containment is vital, especially when regulatory and reputational risks are high.
"Upon detection of a data breach risk, AI models should be immediately disabled from public or partner use, and all relevant parties should be notified as part of a responsible disclosure and containment strategy." While options A and D are longer-term remediation steps and B is investigative, none of them provide the urgent containment that is best practice in such a breach context.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "AI Data Breach and Disclosure Management"
質問 # 52
Which of the following is the GREATEST benefit of integrating AI in ransomware prevention?
- A. Reducing resources required for traditional cybersecurity defenses
- B. Enabling more rapid identification of parties responsible for attacks
- C. Maintaining the confidentiality of threat intelligence analysis
- D. Analyzing large volumes of data to enhance detection of anomalies
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.
質問 # 53
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?
- A. Submitting the same questions in a foreign language translated by another AI-based system
- B. Asking the same questions later when the algorithm has changed after further learning
- C. Randomly placing keywords unrelated to the main topic
- D. Presenting theoretical situations to justify the reason for asking the questions
正解:D
解説:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIATM Study Guide highlights this as a common attack vector in generative AI misuse.
"Adversaries may reframe queries as academic or theoretical scenarios to exploit AI models' reasoning patterns, bypassing ethical constraints. Auditors should test for these circumvention techniques."
質問 # 54
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