ISACA AAIA Prüfungsfrage & AAIA Fragen&Antworten

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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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AAIA Übungsmaterialien & AAIA Lernführung: ISACA Advanced in AI Audit & AAIA Lernguide

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q245-Q250):

245. Frage
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?

Antwort: B

Begründung:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance. According to the ISACA AAIATM Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability. Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.


246. Frage
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?

Antwort: B


247. Frage
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?

Antwort: C

Begründung:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIA™ manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations. Options A, B, and C refer to security attacks, whereas " Model drift " is the natural operational degradation that occurs when AI systems are not regularly updated with fresh, representative data.


248. Frage
Which of the following should be done FIRST when an AI chatbot has been identified as giving harmful advice?

Antwort: C

Begründung:
When a system is actively producing harmful content, the priority is "immediate containment."
"Enabling content output filters" is a critical safety control that acts as a "guardrail," blocking the harmful responses before they reach the end-user. While prompt sanitization (Option A) targets the input, harmful advice often stems from the model's internal logic regardless of the input.
Output filters provide the fastest mitigation for protecting users and the organization's reputation.
Once the immediate risk is contained, the organization can conduct a root-cause analysis and perform more permanent retraining or access segmentation.


249. Frage
Which of the following controls MOST effectively helps to ensure an AI model is resilient against external threats?

Antwort: C

Begründung:
Ensuring AI model resilience against external threats involves validating that the model is configured to resist attacks, such as adversarial inputs, data poisoning, or misuse. The AAIA™ Study Guide emphasizes configuration testing as a crucial control to simulate threat scenarios and assess robustness.
"Model configuration testing simulates real-world threat conditions to validate model resilience. This includes testing against adversarial attacks, input manipulation, and exposure of sensitive outputs." While access monitoring (C) and anonymization (A) reduce risks, they don't actively validate model behavior under threat conditions. Therefore, D offers the most effective resilience measure.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Security and Resilience Testing for AI Models"


250. Frage
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