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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 Fragen Und Antworten, AAIA Trainingsunterlagen

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q71-Q76):

71. Frage
An organization's fraud detection model achieves high accuracy on its initial data set but performs poorly in production. After a complex neural network was trained, the training accuracy was significantly higher than the validation accuracy. Which of the following is the MOST likely cause?

Antwort: D

Begründung:
"Overfitting" occurs when a complex model, such as a deep neural network, learns the "noise" and specific details of the training data rather than the general underlying patterns. A clear indicator of overfitting is a large gap between training performance and validation/test performance. According to the AAIATM manual, this makes the model brittle and unable to generalize to new, unseen data in a production environment. To mitigate this, auditors should recommend techniques such as regularization, dropout, or simplifying the model architecture.


72. Frage
The PRIMARY purpose of maintaining an audit trail in AI systems is to:

Antwort: C


73. Frage
Which of the following is the BEST way to support the development and design of high-risk AI systems?

Antwort: B

Begründung:
The AAIATM Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high-risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase. Therefore, option C is the most effective practice.


74. Frage
A newly deployed fraud detection model is misclassifying transactions due to inconsistent formatting in the data stream. What is the BEST recommendation?

Antwort: A

Begründung:
Inconsistent data formatting means the AI model is receiving inputs that do not match what it was trained to understand. The best corrective action is todocument and enforce technical specificationsfor incoming data (option A).
AAIA highlights this as a fundamental data governance requirement:
* Field formats
* Data types
* Encoding rules
* Required attributes
* Normalization methods
Without strict specifications, the model cannot reliably parse or classify inputs.
Option B does not address the root cause.
Option C improves human response but not data consistency.
Increasing model complexity (D) adds risk and does not fix inconsistent formatting.
References:
AAIA Domain 2: Input Standards and Data Consistency
AAIA Domain 1: Governance and Data Pipeline Controls


75. Frage
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?

Antwort: A

Begründung:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved." While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance-not legal compliance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Rights, Licensing, and Intellectual Property"


76. Frage
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