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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q21-Q26):

NEW QUESTION # 21
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

Answer: A


NEW QUESTION # 22
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?

Answer: B

Explanation:
The risk described is thatcustomer-facing suggestionsmay be inappropriate, insensitive, or offensive. The BEST mitigation is toperform testing of diverse scenarios(B)-including edge cases, demographic variations, and sensitive contexts-to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlightsscenario-based testingandfairness/impact assessmentsas key practices, especially where recommendations directly influence customer interactions.
Increasing data volume (A) does not ensure fairness or sensitivity. Monitoring servers (C) focuses on technical health, not content appropriateness. Threat analysis (D) is important for security but does not directly address emotional or ethical impacts of model outputs. Therefore, structured,diverse scenario testing is the most targeted and effective approach.
References:
ISACA,AAIA Exam Content Outline- Domain 2 & Domain 5: Testing techniques; ethical and user-impact considerations.
ISACA AI guidance on scenario testing for fairness, appropriateness, and user impact.


NEW QUESTION # 23
Which of the following should be done FIRST when developing an incident management process for AI threats?

Answer: C

Explanation:
The AAIA framework states that incident response begins withroles and responsibilities. Without clearly assigned accountability, no classification, escalation, or detection procedures can be effectively implemented.
Defining roles ensures:
* Ownership of monitoring
* Chain of command for incident decisions
* Clear responsibility for documentation
* Communication pathways
* Allocation of resources for containment
Classification (A), escalation (D), and SIEM configuration (C) follow AFTER roles are assigned. Therefore, defining roles and responsibilities is foundational.
References:
AAIA Domain 2: AI Incident Management
AAIA Domain 1: Governance and Accountability Structures


NEW QUESTION # 24
Which role is BEST suited to define the implementation roadmaps for adopting AI solutions?

Answer: A

Explanation:
A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.
AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:
* Organizational strategy
* Technology readiness
* Budget allocations
* Risk appetite
* Regulatory obligations
* Cross-functional impacts
The risk committee (A) focuses only on risk, not implementation. Product management (C) has a narrow scope. Internal audit (D) evaluates controls, not roadmaps.
Thus, the steering committee is the appropriate authority.
References:
AAIA Domain 1: AI Governance Structures
AAIA Domain 4: Strategic Alignment and Oversight


NEW QUESTION # 25
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

Answer: C

Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIA™ Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability." BIA (A) focuses on impact, not data quality. Reviewing SDLC (B) is broad and may not highlight data- specific risks. Penetration testing (C) addresses security, not governance. Therefore, D is the best method.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Quality, Integrity, and Governance Practices"


NEW QUESTION # 26
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