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ISACA AAIA Exam Syllabus Topics:

Section	Weight	Objectives
AI Auditing Tools and Techniques	21%	- Audit Testing and Sampling Methodologies - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques - Data Quality and Analytics for AI Audit - Audit Planning and Design
AI Operations	46%	- Operational Controls and Readiness - Incident Management and Resilience - AI System Lifecycle and Deployment - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation
AI Governance and Risk	33%	- Privacy and Data Governance Programs - AI Governance and Program Management - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - AI Risk Management

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ISACA Advanced in AI Audit Sample Questions (Q230-Q235):

NEW QUESTION # 230
Which of the following is the GREATEST risk associated with the use of AI coding tools by software developers?

A. Excessive reliance on AI tools to accomplish routine development tasks
B. Introduction of security vulnerabilities by AI tools
C. Increased likelihood of human biases in code
D. Difficulty in training developers due to the complexity of AI tools

Answer: B

Explanation:
AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
* Data breaches
* Injection attacks
* Authentication bypasses
* Supply chain compromise
* Privilege escalation
Excessive reliance (A) affects productivity but not security.
Human bias (B) is possible but not as severe as security flaws.
Training complexity (D) is not a risk to AI system integrity.
Therefore, the introduction of security vulnerabilities is the greatest risk.
References:
AAIA Domain 2: AI Risks in Software Development
AAIA Domain 5: Security and Ethical Development Practices

NEW QUESTION # 231
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

A. Assess the response time to the attacks.
B. Evaluate the number of system logs maintained.
C. Validate the process for handling manipulated inputs.
D. Review the speed of learning adaptation.

Answer: C

Explanation:
Adversarial attacks involve "manipulated inputs" (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must "Validate the process for handling manipulated inputs." This includes checking for robust input validation, anomaly detection on incoming data, and "adversarial training" where the model is intentionally exposed to these attacks during development to build resilience.

NEW QUESTION # 232
Which of the following initially provides assurance that the developer correctly interprets and identifies numerical data for balancing prior to inserting into the model?

A. Data dictionary
B. Confusion matrix
C. Statistical summary
D. Data computing library

Answer: A

Explanation:
Adata dictionary(A) is the authoritative source for understanding:
* Data types and numeric formats
* Valid ranges and interpretations
* Field definitions and business meaning
* Normalization and scaling expectations
Before balancing or preprocessing data, developers must verify that they understand each feature correctly.
The AAIA framework emphasizes thatmisinterpretation of numeric variablesoften leads to:
* Incorrect normalization
* Faulty scaling
* Skewed class balancing
* Inaccurate model training
Statistical summaries (C) help identify distributions but cannot validate semantic meaning. Confusion matrices (D) are used after training. Libraries (B) are tools, not sources of interpretation.
References:
AAIA Domain 2: Data Management - Data Dictionaries, Metadata, Data Understanding

NEW QUESTION # 233
Which of the following BEST ensures that an AI system complies with user data ownership rights under privacy regulations?

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

Answer: D

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 # 234
An organization's AI ethics committee is reviewing a new hiring algorithm. Which question is MOST relevant to their review?

A. Does the model's training data or feature selection risk introducing discriminatory bias against protected classes?
B. What cloud provider hosts the model?
C. How much did the model cost to develop?
D. What programming language was used to build the model?

Answer: A

Explanation:
Ethics review in HR/hiring contexts centers on fairness and non-discrimination -- assessing whether training data or features (even indirectly, via proxies) could produce biased outcomes.

NEW QUESTION # 235
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