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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q243-Q248):

NEW QUESTION # 243
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?

Answer: A

Explanation:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIATM Study Guide highlights this as a common attack vector in generative AI misuse.
"Adversaries may reframe queries as academic or theoretical scenarios to exploit AI models' reasoning patterns, bypassing ethical constraints. Auditors should test for these circumvention techniques."


NEW QUESTION # 244
An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?

Answer: C

Explanation:
Accountability for data management (option D) is the most crucial consideration. The AAIA™ Study Guide emphasizes that "clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls." Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Accountability in Data Governance for AI"


NEW QUESTION # 245
Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?

Answer: D


NEW QUESTION # 246
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

Answer: A


NEW QUESTION # 247
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:

Answer: A


NEW QUESTION # 248
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