AAIA Prüfungsfragen Prüfungsvorbereitungen, AAIA Fragen und Antworten, ISACA Advanced in AI Audit

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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Auditing Tools and Techniques
Topic 2: AI Governance and Risk
Topic 3: AI Operations

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q183-Q188):

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

Antwort: D

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"

184. Frage
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Documenting model updates and retraining sessions to ensure traceability
B. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
C. Deploying two separate copies of the model after each adjustment to compare results
D. Assigning a single data science team member to adjust the model in order to establish accountability

Antwort: A

Begründung:
Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIA™ Study Guide states, "Traceability and documentation of all changes-including retraining events, parameter adjustments, and update rationales- enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Change Management and Traceability in AI"

185. Frage
Which of the following is the PRIMARY objective of AI governance?

A. Defining clear roles and responsibilities for AI development, use, and oversight
B. Implementing compliance and ethics controls for AI initiatives
C. Ensuring controls over AI are designed well and operate effectively
D. Promoting a positive return on investment (ROI) from AI projects

Antwort: A

Begründung:
The AAIATM Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment."

186. Frage
The PRIMARY purpose of utilizing neural networks in AI is to:

A. Increase computational power.
B. Minimize maintenance costs.
C. Mimic human decision making.
D. Improve the user interface.

Antwort: C

187. Frage
During an audit of a bank's AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?

A. Conduct an explainability assessment.
B. Evaluate appeal processes.
C. Evaluate transparency controls.
D. Prepare an audit report.

Antwort: C

Begründung:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision- making. If an auditor finds that applicants were not informed, the immediate "First" step is to
"Evaluate transparency controls" to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures.

188. Frage
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