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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q238-Q243):

NEW QUESTION # 238
Which of the following controls would MOST effectively mitigate worst-case service disruption scenarios affecting an AI-based application system?

Answer: A

Explanation:
Including AI-specific disruption scenarios in the organization's Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIA™ Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.
"AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift.
Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime." Tabletop exercises (A) are valuable for preparedness but are less comprehensive than scenario planning. Kill chains (B) are security-specific. KRIs (C) aid in monitoring but don't ensure recovery. Therefore, D offers the most robust mitigation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Business Continuity and Disaster Recovery Planning for AI"


NEW QUESTION # 239
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?

Answer: B

Explanation:
In a high-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. The GREATEST concern for an IS auditor is when the process fails to identify anomalies that can bias training data (A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.


NEW QUESTION # 240
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?

Answer: B


NEW QUESTION # 241
Which of the following is the MOST important risk for an IS auditor to consider when reviewing the adoption of an AI system?

Answer: D


NEW QUESTION # 242
Which of the following AI solutions is BEST used to generate standardized reports using unstructured customer feedback?

Answer: C

Explanation:
Natural Language Processing (NLP) is the branch of AI specifically designed to handle unstructured text. In an audit or business context, NLP tools can perform " sentiment analysis " and " entity extraction " on large volumes of customer feedback to identify common themes, complaints, or positive trends. According to ISACA, NLP is the optimal choice for converting qualitative, human-written text into structured, standardized reports that auditors can use for objective analysis. Anomaly detection (Option A) would only find unusual feedback, and predictive analytics (Option C) would attempt to forecast future feedback, but NLP is the foundational engine required to understand and standardize the existing content.


NEW QUESTION # 243
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