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ISACA AAIA Exam Syllabus Topics:

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

AI Auditing Tools and Techniques

- AI Audit Planning

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

- Audit Testing and Reporting

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

AI Operations

- AI Data Management

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

- AI System Lifecycle

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

AI Governance and Risk

- AI Security and Controls

1. Threat and vulnerability management
2. Vendor and supply chain risk
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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Unsere Prüfungsunterlage zu ISACA AAIA（ISACA Advanced in AI Audit）enthältet alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer Unterlage (Fragen und Antworten) zu ISACA AAIA（ISACA Advanced in AI Audit）ist normalerweise mehr als 98%.

ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q263-Q268):

263. Frage
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?

A. Defining AI policies for model interpretability, transparency, and resiliency
B. Defining roles, responsibilities, and training for all stakeholders on AI ethics
C. Establishing an AI ethics committee or responsible AI review board
D. Developing an AI risk register to record potential algorithmic bias

Antwort: A

Begründung:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the "AI Policies" themselves are the most comprehensive support for governance objectives. Policies provide the "enforceable standards" for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the "Source of Truth" that guides committees and stakeholders in their decision-making processes.

264. Frage
Which control is MOST important to verify in order to ensure proper data management with AI systems?

A. Appropriate data metrics have been developed.
B. A data governance framework has been established.
C. A data inventory has been determined.
D. Data has been labeled according to sensitivity.

Antwort: B

Begründung:
According to the ISACA AAIA™ Study Guide, a " Data Governance Framework " is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization's risk tolerance. It is the " enabling " control that makes all other data quality and security metrics meaningful and enforceable.

265. Frage
A healthcare organization uses data clustering to group patients by medical history for personalized treatment recommendations. Which of the following is the GREATEST privacy risk associated with this practice?

A. The clustering requires more data, increasing the risk of a privacy breach.
B. Irrelevant features in the data may result in inaccurate or biased treatments.
C. Clusters can reveal sensitive personal information depending on how the information is presented.
D. Clustering increases the complexity of the model, making data harder to anonymize.

Antwort: C

Begründung:
Clustering, especially in sensitive domains like healthcare, can inadvertently expose confidential patient data if the resulting groups are too specific or reveal underlying health conditions. The AAIATM Study Guide warns that clustering can increase privacy risks when small, homogenous groups are formed that effectively re-identify individuals or reveal sensitive traits.
"Clustering results must be carefully reviewed to prevent indirect re-identification or unintended exposure of sensitive traits. Ethical handling of aggregated patient data is essential to protect individual privacy."

266. Frage
An organization is training a skin cancer recognition model. Photographs collected from which of the following sources would present the GREATEST risk associated with data integrity?

A. Research facility receiving grants for cancer research
B. Social media platform with images from all over the world
C. Cohort of dermatologists with signed patient consent forms
D. Open-source data augmentation files

Antwort: B

Begründung:
Images fromsocial media platforms(C) pose the greatest risk todata integritybecause:
* Images may be heavily filtered or edited
* Lighting, scale, and resolution are inconsistent
* Metadata is unreliable
* Medical diagnosis labels (if any) are unverified
* Context is unknown and may include misinformation
* Sources cannot be validated or authenticated
AAIA stresses that medical AI systems requirehigh-quality, clinically validated datasetswith clear provenance, accuracy, and consent.
Research facilities (A) and dermatologist cohorts (D) provide medically reliable, high-integrity data.
Open-source augmentation files (B) are synthetic and secondary, but still safer than uncontrolled, unlabeled social media images.
References:
AAIA Domain 2: Data Quality, Provenance, Accuracy, and Label Integrity
AAIA Domain 1: Risk Identification in High-Impact AI Systems

267. Frage
The internal audit department of a large law firm plans to implement an AI solution to review and analyze contracts stored in its enterprise resource planning (ERP) system. Which of the following AI technologies is MOST suitable for this requirement?

A. Deep learning
B. Neural networks
C. Natural language processing (NLP)
D. Machine learning (ML)

Antwort: C

Begründung:
Natural Language Processing (NLP) is the specific branch of AI focused on the interaction between computers and human language. For tasks involving the review, summarization, and extraction of insights from unstructured text documents like legal contracts, NLP is the most suitable technology. It allows the system to understand semantic meaning, identify clauses, and detect inconsistencies within a legal context.

268. Frage
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