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| Section | Weight | Objectives |
|---|---|---|
| AI Auditing Tools and Techniques | 21% | - Audit Testing and Sampling Methodologies - Audit Planning and Design - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques |
| AI Governance and Risk | 33% | - AI Risk Management - Ethics, Regulations, and Standards for AI - AI Governance and Program Management - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs |
| AI Operations | 46% | - AI System Lifecycle and Deployment - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk - Operational Controls and Readiness - Incident Management and Resilience |
ITエリートになるという夢は現実の世界で叶えやすくありません。しかし、ISACAのAAIA認定試験に合格するという夢は、Xhs1991に対して、絶対に掴められます。Xhs1991は親切なサービスで、ISACAのAAIA問題集が質の良くて、ISACAのAAIA認定試験に合格する率も１００パッセントになっています。Xhs1991を選ぶなら、私たちは君の認定試験に合格するのを保証します。
質問 # 161
When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?
正解：C
解説：
When organizations leverage off-the-shelf AI models, effective vendor management is critical to ensure operational reliability, compliance, and long-term support. The ISACA Advanced in AI Audit™ (AAIA™) Study Guide highlights that the "establishment of clear contractual terms regarding responsibilities for ongoing model updates, maintenance, support, and incident response is essential for managing third-party AI risks." By clearly defining the roles and expectations for updates and support (option B), organizations reduce the risk of unaddressed vulnerabilities, outdated models, or unclear recourse in the event of an incident or system failure. This approach supports ongoing risk management and ensures that both parties understand their obligations throughout the model's lifecycle.
While market research, vendor accreditation, and contract review by information security are important due diligence steps, they do not directly address the need for clarity in ongoing vendor responsibilities, which is critical for effective governance and sustained operation of AI solutions.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Vendor Management for AI Systems," Subsection: "Third-Party AI Risk and Contractual Obligations"
質問 # 162
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?
正解：C
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 # 163
Which metric should an IS auditor review to evaluate issues with data collection that could impact AI model training?
正解：B
解説：
The percentage of missing values (option B) directly reflects data collection issues. Missing or incomplete data can degrade model performance, distort feature distributions, and create biased or inaccurate predictions.
AAIA stresses that auditors must evaluate:
* Completeness
* Validity
* Accuracy
* Consistency
Missing values signal failures in upstream processes, including sensors, user inputs, integrations, or data pipelines.
The other metrics are unrelated to raw data integrity:
* Epochs (A) refer to training cycles.
* Percentage of training data (C) concerns dataset partitioning, not quality.
* True positives (D) relate to model performance, not data collection quality.
References:
AAIA Domain 2: Data Quality, Completeness, and Integrity
AAIA Domain 3: Pre-Training Data Validation
質問 # 164
An IS auditor identified that an AI model based on historical data caused " data lag, " leading to poor real- time decision-making. Which of the following is the BEST mitigation strategy?
正解：B
解説：
" Data lag " occurs when the model relies too heavily on old patterns that no longer reflect the current reality (e.g., pre-pandemic shopping habits). To mitigate this, " feature weight updating " is used to give more importance to the most recent data points while " decaying " the influence of older data. This allows the model to adapt more quickly to real-world shifts without requiring a full rebuild from scratch. While data diversity and volume are important for general performance, they do not specifically address the temporal " lag " issue like weight adjustment does.
質問 # 165
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?
正解：B
質問 # 166
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