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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.

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ISACA Advanced in AI Audit Sample Questions (Q305-Q310):

NEW QUESTION # 305
An organization's AI ethics committee is reviewing a new hiring algorithm. Which question is MOST relevant to their review?

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

Answer: D

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 # 306
During an audit of an AI incident response plan, what is the MOST important element specific to AI (beyond a standard IT incident plan)?

A. A communication plan template
B. A designated incident commander
C. Procedures for identifying and containing model-specific failures such as drift, bias incidents, or data poisoning
D. A post-incident report template

Answer: C

Explanation:
Generic IT incident plans do not typically address AI-specific failure modes; a mature plan must explicitly define detection and containment procedures for issues like drift, poisoning, or biased outputs.

NEW QUESTION # 307
Which of the following metrics are the BEST indication of a mature and effective approach to an organization
' s data governance program for its AI systems?

A. Number of AI projects completed within the last fiscal year
B. Percentage of AI models with documented data lineage
C. Frequency of data quality audits on the organization ' s data sets
D. Total budget allocated to AI initiatives across all departments

Answer: B

Explanation:
Documented data lineage (option B) is a cornerstone of mature data governance. The ISACA AAIA™ Study Guide highlights that "effective data governance for AI is characterized by the organization's ability to trace and document the origin, transformation, and use of data throughout its lifecycle and within AI models." This documentation provides transparency, supports accountability, and enables effective risk management.
While regular data quality audits (option C) are important, they do not, by themselves, ensure transparency or traceability. The number of projects (option A) and budget allocation (option D) are not directly indicative of governance maturity.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: " Data Governance in AI: Data Lineage and Traceability "

NEW QUESTION # 308
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?

A. Difficulty of documenting AI algorithm processes
B. System output not being checked for inconsistencies
C. Cascading failures of AI system outputs
D. Inability to identify where an AI system is housed

Answer: B

Explanation:
Unchecked AI outputs pose a significant risk because incorrect or biased results may propagate into decision-making processes. The AAIATM Study Guide stresses that validating AI outputs for consistency, accuracy, and reasonableness is a critical responsibility of both operators and auditors.
"Auditors must verify that appropriate output validation mechanisms are in place. Failure to check outputs increases the likelihood of undetected errors influencing downstream processes or decisions."

NEW QUESTION # 309
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?

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

Answer: A

Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, "algorithmic transparency--enabling auditors and stakeholders to see and understand the rationale behind automated decisions--is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.

NEW QUESTION # 310
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