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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Auditing Tools and Techniques	- Audit Testing and Reporting 1. Audit reporting and follow-up 2. Evidence collection techniques 3. Testing and sampling methodologies - AI Audit Planning 1. AI audit methodologies 2. Audit resource planning 3. Audit scoping and objectives
Topic 2: AI Operations	- AI Data Management 1. Data quality and integrity 2. Training and testing data 3. Operational AI controls - AI System Lifecycle 1. Configuration and change management 2. AI system development and deployment 3. Monitoring and maintenance
Topic 3: AI Governance and Risk	- AI Security and Controls 1. Vendor and supply chain risk 2. Threat and vulnerability management 3. Identity and access management - AI Governance Frameworks 1. AI policies and governance structures 2. AI risk management 3. Regulatory and compliance considerations

Section

Objectives

Topic 1: AI Auditing Tools and Techniques

- Audit Testing and Reporting

1. Audit reporting and follow-up
2. Evidence collection techniques
3. Testing and sampling methodologies

- AI Audit Planning

1. AI audit methodologies
2. Audit resource planning
3. Audit scoping and objectives

Topic 2: AI Operations

- AI Data Management

1. Data quality and integrity
2. Training and testing data
3. Operational AI controls

- AI System Lifecycle

1. Configuration and change management
2. AI system development and deployment
3. Monitoring and maintenance

Topic 3: AI Governance and Risk

- AI Security and Controls

1. Vendor and supply chain risk
2. Threat and vulnerability management
3. Identity and access management

- AI Governance Frameworks

1. AI policies and governance structures
2. AI risk management
3. Regulatory and compliance considerations

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q160-Q165):

160. Frage
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?

A. Robotic process automation (RPA)
B. Autoregressive sequencing model
C. Natural language processing
D. Predictive analytics

Antwort: C

Begründung:
Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIATM Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.
"NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness."

161. Frage
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?

A. Use of open-source intellectual property
B. Model runtime efficiency logs
C. Data performance metrics
D. Data usage agreements

Antwort: D

Begründung:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved." While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance-not legal compliance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Rights, Licensing, and Intellectual Property"

162. Frage
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?

A. Lack of regular quality reviews for training data
B. Inadequate staff training on the use of the system
C. Failure to identify anomalies that can bias training data
D. Infrequent updates to anomaly detection algorithms

Antwort: C

Begründung:
In ahigh-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. TheGREATEST concernfor an IS auditor is when the processfails to identify anomalies that can bias training data(A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.
Option B (lack of regular quality reviews) is serious but is partially addressed if anomaly detection is effective. Option C (infrequent updates) may degrade detection performance over time but is less critical than outright failure to detect harmful anomalies. Option D (staff training) is important for operational effectiveness but still secondary to the technical failure to catch bias-inducing anomalies. AAIA stresses that data integrity and monitoring controlsare paramount in high-risk contexts.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions, data monitoring, and anomaly detection).
ISACA AI risk materials focusing on high-risk AI oversight and data integrity.

163. Frage
An IS auditor is reviewing change documentation of an AI model. Which of the following would pose the GREATEST risk?

A. Management has not tested the model.
B. There is a low number of test scenarios.
C. The seed used for testing is not documented.
D. Test results are not in a standardized format.

Antwort: B

Begründung:
A low number of test scenarios (C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems require broad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.

164. Frage
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

A. The AI system provides multiple options for model training.
B. The AI system uses archived transaction data to provide decisions.
C. The AI system provides transparent justification of decisions.
D. The AI system algorithm uses training data to inform decision output.

Antwort: C

165. Frage
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