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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	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	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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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q226-Q231):

226. Frage
An organization has deployed a generative AI system for customer support that includes frequent updates to the AI model after deployment. Which of the following represents the GREATEST risk?

A. Lack of a dedicated AI governance committee
B. Lack of continuous monitoring for model changes
C. Lack of a change management policy specific to AI
D. Overreliance on manual review of AI model outputs

Antwort: B

Begründung:
When AI models are updated frequently in production,continuous monitoringis critical to detect performance degradation, bias drift, hallucinations, and security issues introduced by new versions. A lack of continuous monitoring (option C) means the organization might not promptly detect harmful behaviors or compliance violations, despite frequent changes, exposing it to operational, reputational, and regulatory risk.
Option A (no AI-specific change management) is serious but can be partially mitigated if effective monitoring reveals issues quickly. Option B (overreliance on manual review) is inefficient but still a control. Option D (no dedicated AI governance committee) is a structural weakness, yet the immediate operational risk is greatest where model changes are not constantly observed. AAIA emphasizessupervision of AI solutionsand monitoring of outputs and impacts, which are directly undermined when continuous monitoring is absent.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions; Change Management Specific to AI).
ISACA materials on continuous monitoring and post-deployment oversight of AI systems.

227. Frage
Which of the following BEST demonstrates effective coordination to ensure comprehensive oversight of an AI system deployed across multiple jurisdictions?

A. Establishing joint oversight plans and communication channels between agencies
B. Centralizing responsibility under a single supervisory authority
C. Focusing oversight activities on technical anomaly detection metrics
D. Relying on automated processes for anomaly detection and documentation

Antwort: A

Begründung:
When AI systems spanmultiple jurisdictions, they are subject to different regulatory regimes, cultural expectations, and risk tolerances. AAIA emphasizes that oversight must be coordinated to avoid gaps or overlaps.Establishing joint oversight plans and communication channels between agencies(B) ensures that relevant authorities share information, align on expectations, and collectively monitor AI risks, enabling coherent and comprehensive oversight.
Option A focuses too narrowly on technical metrics without ensuring cross-jurisdiction coordination. Option C may not be feasible or lawful, as jurisdictional sovereignty often prevents centralizing authority. Option D emphasizes automation but does not address governance and coordination. Thus, the best demonstration of effective oversight for cross-jurisdiction AI deployments isformal joint oversight and structured communication.
References:
ISACA,AAIA Exam Content Outline- Governance of AI (roles, responsibilities, coordination among stakeholders).
ISACA materials addressing multi-jurisdictional AI risk, regulatory alignment, and oversight structures.

228. Frage
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

A. Data versioning controls were not developed.
B. The hyperparameters are not optimized.
C. Compute scale training was not performed.
D. Consent is not properly managed.

Antwort: B

Begründung:
False positives in fraud detection AI systems often stem from poorly optimized hyperparameters.
Hyperparameters control aspects of the model's learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.
"Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition." Options A and B relate to data governance but do not directly cause false positives in predictions. Option C (compute scale training) may affect model efficiency, not accuracy. Thus, D is the most appropriate answer.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Tuning and Optimization"

229. Frage
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

A. Identifying technology-specific risk and considerations
B. Developing architecture diagrams
C. Identifying separation of duties conflicts for database data changes
D. Summarizing meeting transcripts from interviews with database administrators (DBAs)

Antwort: A

Begründung:
Generative AI excels at synthesizing large datasets and technical documentation into understandable insights.
The AAIA™ Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.
"AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping." While summarizing interviews (D) and creating diagrams (B) are helpful, only C directly informs audit planning with actionable intelligence. A (separation of duties) is a later-stage control assessment.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Use in Planning and Scoping"

230. Frage
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?

A. Unlabeled data sets
B. Randomized data sets
C. Clustered data sets
D. Labeled data sets

Antwort: D

Begründung:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIATM Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.

231. Frage
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