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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - Audit Evidence Collection Techniques - Audit Planning and Design - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting - Audit Testing and Sampling Methodologies |
| Topic 2: AI Governance and Risk | 33% | - AI Risk Management - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - AI Governance and Program Management - Ethics, Regulations, and Standards for AI |
| Topic 3: AI Operations | 46% | - Operational Controls and Readiness - AI System Lifecycle and Deployment - Incident Management and Resilience - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk |
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질문 # 92
Which of the following is the BEST reason that recurrent neural networks enable language translation of documents?
정답：A
설명：
Recurrent neural networks (RNNs) and their variants (such as LSTMs and GRUs) are designed to handle sequential data, capturing dependencies across time or position in a sequence. In language translation, words and phrases must be interpreted in context, where the meaning of a word depends on preceding (and, in advanced architectures, following) tokens. RNNs maintain internal state across steps, allowing the model to encode information from earlier parts of the sentence when predicting later outputs.
Option B (association rules) refers more to classical data#mining methods, not the core reason RNNs work for translation. Option C (grid data) is more relevant to convolutional neural networks used for images. Option D (unidirectional) is not inherently an advantage; in fact, bidirectional models are often preferred. Therefore, the key property enabling RNN use in translation is thesequential processingcapability.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Models, Considerations, and Requirements (types of AI, machine learning models).
ISACA, general AI fundamentals content used in AAIA preparation (sequence models for NLP).
질문 # 93
An IS auditor is reviewing a dataset used by a university. Which of the following MOST likely indicates a risk that the model could not process all data and make necessary correlations?
정답：A
설명：
In machine learning, "Data Typing" is foundational. If a numerical field (like Grade Percent) is stored as an "Object" (string/text), the AI model cannot perform mathematical operations on it, such as calculating correlations or averages. According to the AAIATM manual, this is a "Data Quality" failure that leads to "Feature Exclusion," where the model simply ignores the variable or treats each number as a unique text label, losing all quantitative meaning. The other formats (Integer, Float, Boolean) are appropriate for their respective data types.
질문 # 94
Which use case for an AI model to be used by a food delivery service would pose ethical risk to the organization?
정답：D
설명：
Using AI to make employment decisions such as driver termination or retention introduces significant ethical risks. If based solely on performance metrics without context or human review, such systems can lead to unfair treatment or discrimination-violating principles of transparency and due process.
"Automating workforce decisions must be approached cautiously to prevent discriminatory outcomes. Ethical AI governance requires oversight when AI is used for employment-impacting decisions." A, C, and D involve business optimization without directly affecting individual employment rights. Therefore, B poses the greatest ethical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Human Impact and Workforce Automation Ethics"
질문 # 95
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?
정답：D
설명：
To determine whether an AI model's outputs areeffectively controlled for bias, an auditor needsempirical performance evidenceacross demographic groups.Similar accuracy ranges across groups (A)demonstrate that the model performs equitably and does not disproportionately advantage or disadvantage specific populations. This aligns with AAIA's emphasis onfairness metrics, such as disparate error rates, equal opportunity, and demographic parity analyses.
Option B (fairness definition) is important for governance but does not prove fairness in practice. Option C may introduce historical human bias rather than mitigate it. Option D (transparency of development) supports explainability but does not validate fairness outcomes. Actualperformance parity across demographic groupsis the strongest evidence of bias control.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (fairness evaluation; bias assessments).
ISACA fairness guidance emphasizing cross-group performance comparison.
질문 # 96
Which of the following considerations should be prioritized when using an AI tool to select a sample for conducting an audit of a financial institution's transaction processing system?
정답：B
설명：
In an audit context,transparencyof sampling is essential for demonstrating that the sample is fair, unbiased, and aligned with the audit objectives. When an AI tool selects samples for testing financial transactions, auditors must be able to explain and defendhowthe sample was generated-particularly to management, regulators, and external stakeholders. Option A directly supports AAIA's focus onaudit planning, sampling methodologies, and AI audit evidence.
High throughput (option B) and speed (option C) are beneficial but secondary to methodological soundness and explainability. Option D (historical performance) can be helpful but does not guarantee current transparency or appropriateness in new contexts. For AI-enabled sampling, the priority is that theselection logic is understandable, documented, and reproducible, ensuring audit defensibility.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (Audit Testing and Sampling Methodologies; Audit Evidence Collection Techniques).
ISACA auditing guidance on sampling and transparency in AI-assisted audit procedures.
질문 # 97
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