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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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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AAIA Prüfungsressourcen: ISACA Advanced in AI Audit & AAIA Reale Fragen

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q113-Q118):

113. Frage
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:

Antwort: C

Begründung:
AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIA™ Study Guide states, "AI-based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually." AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Advantages of AI-Enabled Audit Approaches"


114. Frage
An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?

Antwort: D

Begründung:
Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIA™ Study Guide, when input exceeds token limits, the model processes only the initial portion-leading to biased outputs based on early entries.
"Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset." Options A and C describe side effects, but D identifies the most significant risk-output distortion due to truncated input.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Limitations of Generative AI Models"


115. Frage
Which of the following components would MOST effectively address cumulative benefits as part of reinforcement learning (RL)?

Antwort: B

Begründung:
In Reinforcement Learning, the goal is to maximize long-term rewards. While the " Reward Function " provides immediate feedback for an action, the " Value Function " estimates the " cumulative benefit " or the total expected reward an agent can gain from a particular state moving forward. For an auditor, understanding the value function is crucial because it governs the agent ' s long-term strategy and decision-making logic. If the value function is poorly defined, the AI might take actions that yield immediate points but lead to long- term failure or unethical shortcuts.


116. Frage
During a walkthrough, an auditor learns that model retraining occurs automatically without any approval gate. What is the GREATEST risk this presents?

Antwort: A

Begründung:
Automated retraining without a human approval/validation gate can silently push a degraded or biased model into production, bypassing change management controls.


117. Frage
An IS auditor is interviewing management about implemented controls around machine learning (ML) models deployed in the production environment. Which of the following schedules for reviewing the performance of a deployed model would be of GREATEST concern to the auditor?

Antwort: C

Begründung:
Only reviewing an ML model's performance one time prior to migrating to production (option C) is of greatest concern. The AAIA™ Study Guide emphasizes that "AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors." A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.
Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Continuous Monitoring and Review of Deployed AI Models"


118. Frage
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