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ISACA AAIA Prüfungsplan:

Thema	Einzelheiten
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	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 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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ISACA AAIA Quiz - AAIA Studienanleitung & AAIA Trainingsmaterialien

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q126-Q131):

126. Frage
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations.
Which of the following MOST effectively detects data drift related to the model's predictions?

A. Comparing incoming patient data distributions with the training data set
B. Conducting periodic model retraining to ensure alignment with updated patient data
C. Applying overrides to allow healthcare professionals to correct the AI model's recommendations
D. Using adversarial testing to simulate scenarios that stress test the model's predictions

Antwort: A

Begründung:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time.
According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide,data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained.
If not detected, data drift can degrade model performance and lead to erroneous predictions.
Themost effective approach to detect data driftis tocontinuously compare the statistical distributions of incoming (production) data with those of the training dataset. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIA™ Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs." While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness,they do not inherently detect data drift-they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and Maintenance," Subsection: "Detection and Management of Data Drift"

127. 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. Focusing oversight activities on technical anomaly detection metrics
C. Centralizing responsibility under a single supervisory authority
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.

128. Frage
An organization is adopting AI for its procurement and inventory teams, raising concern from stakeholders that they will lose their jobs due to AI. Which of the following is the BEST way for the IS auditor to assess whether the potential negative impacts were minimized?

A. Review human-centered design practices to determine how they were considered.
B. Review how the project management team collected feedback in engagement activities.
C. Review the current state assessment of how AI may impact the organization.
D. Review the AI roadmap for short-term and long-term milestones.

Antwort: A

Begründung:
Human-centered design (HCD) focuses on integrating stakeholder needs, ethical implications, and social impact into AI system development. The AAIATM Study Guide emphasizes the role of HCD in minimizing negative workforce impacts and ensuring inclusive design.
"Auditors should review whether the AI system design process included human-centered practices-- particularly for applications affecting jobs or critical human roles. HCD ensures responsible adoption."

129. Frage
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management's PRIMARY response?

A. Accept the audit findings as within risk tolerance.
B. Synthesize more data of the affected demographic group.
C. Conduct comprehensive bias analysis.
D. Determine if audit sampling is sufficient.

Antwort: C

Begründung:
A significantly higher rejection rate is a clear indicator of potential algorithmic discrimination.
Management's PRIMARY response should be to conduct a comprehensive bias analysis (C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.

130. Frage
An IS auditor is reviewing a dataset related to a restaurant ' s food delivery process. Which of the following data preparation techniques should be used to ensure that no single feature dominates and all fields are weighted appropriately?

A. Inserting average values where Distance data is missing
B. Removing duplicate entries for Customer Address field
C. Converting Postal Code values from text format to integers
D. Scaling the Distance From Restaurant field

Antwort: D

Begründung:
In many AI models (like K-means or SVM), features with larger numerical values can " dominate " the model
' s logic simply because they are mathematically larger, not because they are more important. For example, " Distance " (measured in thousands of meters) will outweigh " Delivery Rating " (measured from 1-5). " Scaling " (or normalization) converts all numeric features into a common range (e.g., 0 to 1). This ensures that the model treats all fields with appropriate weight based on their actual significance. Converting postal codes to integers (Option A) is actually a risk because it implies a false numerical order to geographic locations.

131. Frage
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