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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.

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ISACA Advanced in AI Audit Sample Questions (Q116-Q121):

NEW QUESTION # 116
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

A. Identifying technology-specific risk and considerations
B. Summarizing meeting transcripts from interviews with database administrators (DBAs)
C. Developing architecture diagrams
D. Identifying separation of duties conflicts for database data changes

Answer: A

Explanation:
Generative AI excels at synthesizing large datasets and technical documentation into understandable insights. The AAIATM Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.
"AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping."

NEW QUESTION # 117
Which of the following is the MOST essential attribute of an AI-driven audit tool?

A. Explainability of model conclusions
B. Support for a range of statistical methods
C. Optimization of audit resources
D. Use of labeled training datasets

Answer: A

Explanation:
According to the ISACA AAIA™ Study Guide, transparency is the cornerstone of AI auditing. For an auditor to rely on an AI-driven tool, the tool must provide " explainability " -the ability to describe how a specific conclusion or anomaly was identified. Without explainability, the AI remains a " black box, " preventing the auditor from exercising professional skepticism or justifying audit findings to stakeholders. While optimizing resources and supporting statistics are beneficial, they do not provide the necessary assurance that the model ' s logic is sound, unbiased, and compliant with regulatory standards. Auditors are required to validate the rationale behind automated decisions to ensure accountability.

NEW QUESTION # 118
When an IS auditor uses generative AI with external RAG (retrieval-augmented generation) to gather evidence during an audit, which of the following poses the GREATEST data security risk?

A. External search engines only respond to public data.
B. Sensitive internal context may be included in queries sent to external services.
C. Personal information may be shared based on model training data.
D. The model might fail to retrieve data from the vector.

Answer: B

Explanation:
The GREATEST risk is thatsensitive internal contentmay be embedded in AI prompts and sent to an external system (A), exposing confidential or regulated information. Retrieval-augmented generation often involves sending queries or embeddings to third-party APIs, creating a significantdata leakagerisk if internal context is included.
Option B deals with training-data leakage, which is notable but less immediate. Option C is irrelevant to the core risk. Option D concerns operational reliability, not security. In AAIA, protection of internal audit evidence and sensitive data is paramount, and theprimary threat is external disclosure through prompts.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Data Protection, AI Privacy Risks.

NEW QUESTION # 119
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

A. To ensure the systems align with the preservation of individual rights
B. To improve the accuracy and performance of the systems
C. To identify and mitigate potential data drift within models
D. To align AI system development with organizational values and principles

Answer: A

NEW QUESTION # 120
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?

A. Reinforcement learning
B. Unsupervised learning
C. Federated learning
D. Supervised learning

Answer: B

Explanation:
Unsupervised learning uses unlabeled data to discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations where labels are unavailable or costly, yet insight is still needed.

NEW QUESTION # 121
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