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ISACA AAIA Exam Syllabus Topics:

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

Section

Objectives

Topic 1: AI Auditing Tools and Techniques

- Audit Testing and Reporting

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

- AI Audit Planning

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

Topic 2: AI Governance and Risk

- AI Governance Frameworks

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

- AI Security and Controls

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

Topic 3: AI Operations

- AI System Lifecycle

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

- AI Data Management

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

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ISACA Advanced in AI Audit Sample Questions (Q115-Q120):

NEW QUESTION # 115
Which of the following BEST supports reproducibility of AI model results during an audit?

A. A well-written user manual
B. A large training dataset
C. High model accuracy
D. Version control of code, data, and model parameters

Answer: D

Explanation:
Reproducibility requires that the exact code, data snapshot, hyperparameters, and environment used to produce a result can be retrieved and rerun -- this is achieved through rigorous versioning practices (MLOps).

NEW QUESTION # 116
A retail organization uses an AI model to analyze customers' purchase history in order to offer personalized discounts. Which of the following practices represents the MOST ethical use of customer data?

A. Providing the public with access to review and audit the data set of collected customer information
B. Retaining and analyzing all available customer data to ensure unbiased recommendations
C. Utilizing customer purchase data only after obtaining explicit consent and allowing customers to opt out
D. Sharing customer purchase data with third-party vendors to improve advertising and communication

Answer: C

Explanation:
The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIATM Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.
"Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights."

NEW QUESTION # 117
Which of the following BEST supports the principle of "least privilege" in an AI system's architecture?

A. A single shared credential is used for all AI system components
B. Access to training data, model parameters, and deployment pipelines is restricted based on role and necessity
C. API keys are never rotated
D. All data scientists have administrative access to production models

Answer: B

Explanation:
Least privilege limits access to only what is necessary for a given role, reducing the attack surface and risk of unauthorized model tampering, data exfiltration, or accidental misconfiguration.

NEW QUESTION # 118
Which of the following BEST ensures that an AI system complies with user data ownership rights under privacy regulations?

A. Regularly conducting AI system performance testing for accuracy
B. Applying data clustering techniques to anonymize data sets
C. Implementing a transparent data consent management process
D. Enforcing strict data retention policies to limit storage duration

Answer: C

Explanation:
A transparent data consent management process ensures users are informed about how their data will be used, and enables them to exercise their rights to consent, access, rectify, or delete their data. This is a core requirement under regulations such as GDPR and CCPA.
"Consent management is fundamental to respecting data ownership rights. Organizations must clearly disclose data usage purposes, provide opt-in/out capabilities, and maintain audit trails of user interactions."

NEW QUESTION # 119
An IS auditor reviews an AI tool using K-means to cluster customers. One cluster shows very high spending but low product diversity. What should the auditor recommend?

A. Document the algorithm failed because high spending customers did not exhibit high product diversity.
B. Treat the cluster as a potentially valid segment of loyal customers with limited product interest.
C. Increase the number of clusters to better capture variations in spending behavior.
D. Replace K-means clustering with a supervised learning model for more accurate analysis.

Answer: B

Explanation:
K-means clustering is anunsupervised learningtechnique that groups data based on similarity. Discovering a cluster withhigh spending but low product diversityis a plausible and meaningful business insight: it may representloyal customers who repeatedly purchase a narrow range of products. The auditor should therefore recommend treating this cluster as apotentially valid segment(B), subject to further business analysis and controls where appropriate.
Option A is incorrect because this pattern does not imply algorithm failure. Option C (adding more clusters) might overcomplicate the segmentation without evidence that the current clustering is deficient. Option D misunderstands the purpose of clustering; a supervised model would require labeled outcomes and is not necessarily "more accurate" for exploratory segmentation. AAIA's content on AI in audit processes stresses that auditors must interpret AI-driven insights critically, not assume anomalies equal errors.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes (AI tools, use of clustering and analytics in audit).
ISACA analytics guidance on interpreting unsupervised learning outcomes in an audit context.

NEW QUESTION # 120
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