Echte AAIA Fragen und Antworten der AAIA Zertifizierungsprüfung

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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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 AAIA Prüfungsfragen mit Lösungen (Q119-Q124):

119. Frage
An organization is using a large language model (LLM) to assist in evaluating loan applications, but the training data used is known to be incomplete. Which of the following is the GREATEST associated risk?

Antwort: D

Begründung:
Incomplete training dataoften leads to underrepresentation of certain applicant types, products, or scenarios.
In credit and lending, this typically translates intosystematic bias: some groups are evaluated on richer historical patterns, while others are evaluated on sparse or unrepresentative information. The greatest associated risk is thereforeunfair loan decisions(A), which can manifest as unjustified rejections, inappropriate pricing, or inconsistent risk assessments.
While delays (B), reduced satisfaction (C), or increased manual work (D) may occur, they are secondary operational issues. AAIA highlights that for financial services, the central risks includefairness, discrimination, regulatory compliance, and reputational impact. Incomplete data directly undermines fairness and can violate lending regulations and internal risk appetite.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (risk categories, including fairness and discriminatory outcomes).
ISACA AI ethics content on data completeness and representativeness in decisioning systems.


120. Frage
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?

Antwort: B

Begründung:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.


121. Frage
A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model's decisions to stakeholders?

Antwort: D


122. Frage
An IS auditor is evaluating a large language model (LLM) before deployment. Which of the following is the MOST secure way to manage agency for the model?

Antwort: D

Begründung:
" Agency " refers to the model ' s ability to take actions or access data. LLMs are non-deterministic and can be tricked via " prompt injection " to ignore their internal rules. Therefore, " Authorization and privilege checks " must be performed by a separate, deterministic security layer that is " independent of the LLM. " According to the ISACA AAIA™ Study Guide, you should never allow an AI to decide its own permissions (Option D) or those of other systems. If a user asks an AI to delete a file, the AI should simply " request " the deletion, and a standard, non-AI security system should check if the user has the right to do so. This maintains the " Principle of Least Privilege " and prevents unauthorized actions via model manipulation.


123. Frage
When developing an audit plan, which of the following is MOST important specifically for the transparency of an AI application?

Antwort: B

Begründung:
Explainability testing is essential for determining how and why an AI model produces specific outputs.
Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
* Stakeholders understand model decisions
* Auditors can verify fairness and accuracy
* Regulators can review decision logic
* The organization can justify outcomes to affected individuals
* Models align with ethical and governance expectations
Regression testing (B) checks consistency across versions.
Compliance testing (C) checks legal adherence.
Validation testing (D) checks accuracy.
None specifically ensure transparency.
References:
AAIA Domain 5: Explainability, Transparency, Accountability
AAIA Domain 3: Auditability of AI Systems


124. Frage
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