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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Audit Evidence Collection Techniques - Audit Testing and Sampling Methodologies - AI Audit Outputs and Reporting - Data Quality and Analytics for AI Audit |
| Topic 2: AI Governance and Risk | 33% | - AI Risk Management - Privacy and Data Governance Programs - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - AI Governance and Program Management |
| Topic 3: AI Operations | 46% | - Incident Management and Resilience - Operational Controls and Readiness - AI System Lifecycle and Deployment - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation |
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NEW QUESTION # 23
Which of the following is the BEST approach to mitigate the risk of "AI model degradation"?
Answer: A
Explanation:
Model degradation occurs as the "Freshness" of the training data wanes and real-world conditions evolve. The most robust control is "Periodic human reviews" (Human-in-the-Loop).
Human experts can identify "drift" in logic or common-sense failures that automated systems might miss.
NEW QUESTION # 24
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?
Answer: C
Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"
NEW QUESTION # 25
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?
Answer: B
Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."
NEW QUESTION # 26
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?
Answer: D
Explanation:
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.
NEW QUESTION # 27
Which role is BEST suited to define the implementation roadmaps for adopting AI solutions?
Answer: B
Explanation:
A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.
AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:
Organizational strategy
Technology readiness
Budget allocations
Risk appetite
Regulatory obligations
Cross-functional impacts
NEW QUESTION # 28
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