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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 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.
Topic 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.

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ISACA Advanced in AI Audit Sample Questions (Q310-Q315):

NEW QUESTION # 310
An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?

A. Gaps in AI privacy compliance and accountability
B. Deficiencies in policies and procedures validating AI model accuracy
C. Limited documentation of user access permissions
D. Excessive dependence on automated data collection and cleansing

Answer: A

Explanation:
When integrating data from multiple external sources, unclear data ownership directly affects accountability for privacy, consent, retention, and lawful processing. This creates gaps in AI privacy compliance and accountability (option D), which is the greatest concern because violations can lead to regulatory sanctions, litigation, and serious reputational damage. AAIA's governance and risk domain emphasizes privacy and data governance programs, including clear roles, responsibilities, and ownership.

NEW QUESTION # 311
The internal audit department of a large law firm plans to implement an AI solution to review and analyze contracts stored in its enterprise resource planning (ERP) system. Which of the following AI technologies is MOST suitable for this requirement?

A. Neural networks
B. Natural language processing (NLP)
C. Deep learning
D. Machine learning (ML)

Answer: B

Explanation:
Natural Language Processing (NLP) is the specific branch of AI focused on the interaction between computers and human language. For tasks involving the review, summarization, and extraction of insights from unstructured text documents like legal contracts, NLP is the most suitable technology. It allows the system to understand semantic meaning, identify clauses, and detect inconsistencies within a legal context.

NEW QUESTION # 312
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

A. The model utilizes historical credit data to predict future credit behavior.
B. The model uses postal codes as a primary factor in determining creditworthiness.
C. The model incorporates the applicant's loan history to assess spending habits.
D. The model considers the applicant's income level as a key factor in the credit decision.

Answer: B

NEW QUESTION # 313
Which of the following BEST detects model drift or unexpected changes in AI model outputs?

A. AI model retraining
B. Standardization of AI configurations
C. AI model documentation reviews
D. Anomaly monitoring

Answer: D

Explanation:
Anomaly monitoring detects irregularities or deviations in input data or model outputs, which are key indicators of model drift. According to the AAIATM Study Guide, continuous anomaly detection is one of the most effective methods for identifying when a model is no longer functioning as expected due to changes in the data environment.
"Monitoring for output anomalies enables early identification of model drift. This proactive approach allows organizations to retrain or adjust models before significant performance degradation occurs."

NEW QUESTION # 314
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

A. The AI system provides transparent justification of decisions.
B. The AI system uses archived transaction data to provide decisions.
C. The AI system algorithm uses training data to inform decision output.
D. The AI system provides multiple options for model training.

Answer: A

Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, "algorithmic transparency-enabling auditors and stakeholders to see and understand the rationale behind automated decisions-is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
By contrast, options A, B, and D are either standard operational aspects or supportive features, but they do not directly empower auditors to evaluate or scrutinize the algorithm's decision-making process. The Study Guide explicitly highlights, "Effective AI governance frameworks require that systems provide clear, interpretable explanations for their decisions, supporting auditability and accountability across the AI lifecycle." Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Transparency and Explainability in AI Systems," Subsection: "Auditability and Accountability"

NEW QUESTION # 315
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