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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Certificate Validity Period:	3 years
Exam Duration:	150 minutes
Exam Format:	Scenario-based questions, Multiple-choice questions, Computer-based testing
Passing Score:	450 (scaled, range 200–800)
Exam Price:	US$459 (member) / US$599 (non-member)
Real Exam Qty:	90
Available Languages:	Spanish, English, Chinese
Related Certifications:	FCCA CIA CISA ACCA CPA
Recommended Training:	AAIA Review Manual AAIA Official Review Course
Exam Registration:	ISACA Official Registration
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:	Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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AAIA Übungstest: ISACA Advanced in AI Audit & AAIA Braindumps Prüfung

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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.

ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q179-Q184):

179. Frage
Which of the following is the BEST approach to mitigate the risk of "AI model degradation"?

A. Periodic human reviews of model outputs and recommendations
B. Training models on both model-generated and human-generated data
C. Increasing the use of synthetic data for model training
D. Continuous monitoring to identify adversarial inputs

Antwort: A

Begründung:
Model degradation occurs as the "Freshness" of the training data wanes and real-world conditions evolve. The most robust control is "Periodic human reviews" (Human-in-the-Loop).
Human experts can identify "drift" in logic or common-sense failures that automated systems might miss.

180. Frage
The PRIMARY objective of auditing AI systems is to:

A. Optimize user experience and interface satisfaction.
B. Minimize algorithm latency and information storage impacts.
C. Maximize system efficiency and throughput.
D. Identify biases and decision transparency.

Antwort: D

Begründung:
The AAIA™ Study Guide outlines the primary goal of auditing AI systems as ensuring that they operate ethically, transparently, and fairly. This includes identifying potential biases in decision-making and confirming that the rationale behind outcomes can be explained and understood.
"The key focus of an AI audit is to evaluate whether the system performs within the parameters of fairness, legality, and accountability. This involves testing for bias and ensuring decision-making transparency." Options B, C, and D refer to performance and usability concerns, which are secondary to the ethical and governance-focused purpose of AI audits.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Purpose and Scope of AI Audits"

181. Frage
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?

A. Inadequate documentation for AI tools
B. Use of AI to automate routine manual tasks
C. Lack of transparency in AI algorithms
D. Narrowly targeted AI awareness training

Antwort: C

Begründung:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the
"Lack of transparency in the algorithm" creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a "Black Box," it may consolidate decision- making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.

182. Frage
An internal audit department notices that AI-generated audit reports are producing false conclusions. Which of the following is the BEST way to correct this issue?

A. Increase the model context.
B. Update service level agreements (SLAs).
C. Decrease the model's creativity score.
D. Suspend utilization of the tool until resolved.

Antwort: D

Begründung:
If AI-generated audit reports containfalse conclusions, the integrity of audit evidence and overall assurance is compromised. According to AAIA, when AI systems compromiseaccuracy of audit outputs, the immediate step should be tosuspend use(B) to prevent further incorrect or misleading reporting.
This allows auditors to:
* Investigate the root cause of incorrect conclusions
* Validate the model's training data, rules, and prompt structures
* Reassess controls, safeguards, and human review processes
* Prevent reliance on unreliable AI-generated audit material
Increasing context (A) or reducing creativity (C) may help generative models but do not guarantee correction of fundamental errors. Updating SLAs (D) is administrative and does not solve the immediate integrity issue.
References:
AAIA Domain 3: Audit Evidence, Professional Skepticism, and AI output validation.
AAIA Domain 2: Monitoring AI Outputs for Accuracy.

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

Antwort: A

Begründung:
A significantly higher rejection rate is a clear indicator of potentialalgorithmic discrimination. Management' s PRIMARY response should be to conduct acomprehensive 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.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help butonly afterthe root cause is identified-it is not the primary first step.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Bias, Fairness, and Transparency Evaluations.

184. Frage
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