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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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 AAIA Prüfungsfragen mit Lösungen (Q75-Q80):

75. Frage
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

Antwort: A


76. Frage
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?

Antwort: A

Begründung:
A " Model Card " is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear " Understanding of model intent and performance context. " It allows the organization to determine if a vendor ' s model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition. While it supports documentation compliance (Option A), its core value lies in providing the transparency necessary for informed decision- making and governance.


77. Frage
An IS auditor is evaluating a cybersecurity system that uses agentic AI for autonomous threat detection and incident response. Which of the following is MOST important for the auditor to consider?

Antwort: C

Begründung:
" Agentic AI " goes beyond simple detection; it can take actions (like shutting down a server) autonomously.
The greatest risk is that the AI might misinterpret a legitimate but unusual business activity as a threat and trigger a " kill switch, " causing massive, unplanned downtime. This is a significant operational and availability risk. Auditors must ensure there are " guardrails " or a " human-in-the-loop " for high-impact actions. While reduced intervention (Option B) is a benefit, it is also the source of the risk that the auditor must mitigate through governance and control evaluation.


78. Frage
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?

Antwort: A

Begründung:
Prompt engineering manipulation often involves disguising inappropriate questions in hypothetical or conditional formats, tricking the AI system into bypassing its filters. The AAIA™ 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." Other methods (A, C, D) may work under specific conditions, but B is the most widely effective and documented prompt-engineering technique.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Prompt Engineering and Generative AI Risks"


79. Frage
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?

Antwort: C

Begründung:
The greatest challenge for IS auditors in evaluating the explainability of generative AI models is the changing nature of algorithms as AI continues to learn (option D). Generative AI models, especially those using advanced techniques like deep learning and reinforcement learning, often employ continuous or dynamic learning, which results in models that evolve over time. This adaptability can significantly hinder explainability because the logic, parameters, or decision pathways may shift with ongoing retraining or real-time learning.
The ISACA Advanced in AI AuditTM (AAIATM) Study Guide stresses that: "Continually learning AI systems present unique audit challenges, as their internal representations and reasoning can change after deployment, making it difficult to fully capture and explain the rationale for outputs at any given point."


80. Frage
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