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ISACA AAIA Prüfungsplan:

Thema	Einzelheiten
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 (Q152-Q157):

152. Frage
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card - Electrical Grid Predictive Maintenance Model
Model Information:
Description: AI model designed to predict maintenance needs for electrical grid components, reduce unplanned downtime, and improve grid reliability.
Inputs: Real-time sensor data, historical maintenance records, and operational logs.
Outputs: Maintenance needs predictions for 60 & 90 days.Evaluation:
Approach: Cross-validation and validation of accuracy, precision, and recall.
Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%

A. Grid failure is predicted to occur after 90 days.
B. The electrical grid uptime is expected to be 72% of the time.
C. The AI model correctly predicts maintenance needs 95% of the time.
D. F1 indicates that the model identifies true maintenance needs 76% of the time.

Antwort: D

153. Frage
What is the GREATEST risk of relying solely on aggregate accuracy as the primary success metric for a deployed AI model?

A. It is too difficult for auditors to understand
B. It cannot be calculated for classification models
C. It can mask significant performance disparities across subgroups or specific use cases
D. It requires too much computing power to calculate

Antwort: C

Begründung:
Aggregate accuracy can be high overall while performance varies significantly for specific subpopulations or edge cases -- a key reason disaggregated and fairness-specific metrics are also needed.

154. Frage
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?

A. AI bias
B. Transparency
C. AI model hallucinations
D. Disclosure of personal information

Antwort: D

Begründung:
The use of customer data in AI training presents a significant privacy risk, especially when the data is not properly anonymized or when consent has not been explicitly obtained. The AAIA™ Study Guide classifies the unauthorized or inadvertent disclosure of personally identifiable information (PII) as the most severe risk in such contexts.
"Training AI models on customer data without proper safeguards can result in the unintentional exposure of personal or sensitive information, leading to data breaches and compliance violations." While bias (B) and hallucinations (D) are important operational concerns, they do not pose the same regulatory and ethical severity as PII exposure. Therefore, A is the most critical risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy Risks and Data Minimization"

155. Frage
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

A. Reviewing the organization's AI software development life cycle documentation
B. Conducting a penetration test to identify vulnerabilities in the model
C. Performing a business impact analysis (BIA) to assess the consequences of AI model failure
D. Assessing data lineage to verify the traceability of data sources

Antwort: D

Begründung:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability." BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data- specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Quality, Integrity, and Governance Practices"

156. Frage
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?

A. Difficulty of documenting AI algorithm processes
B. System output not being checked for inconsistencies
C. Cascading failures of AI system outputs
D. Inability to identify where an AI system is housed

Antwort: B

157. Frage
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