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ISACA AAIA Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Auditing Tools and Techniques- AI Audit Planning
  • 1. Audit resource planning
  • 2. AI audit methodologies
  • 3. Audit scoping and objectives
- Audit Testing and Reporting
  • 1. Audit reporting and follow-up
  • 2. Testing and sampling methodologies
  • 3. Evidence collection techniques
Topic 2: AI Governance and Risk- AI Security and Controls
  • 1. Vendor and supply chain risk
  • 2. Threat and vulnerability management
  • 3. Identity and access management
- AI Governance Frameworks
  • 1. Regulatory and compliance considerations
  • 2. AI risk management
  • 3. AI policies and governance structures
Topic 3: AI Operations- AI System Lifecycle
  • 1. Monitoring and maintenance
  • 2. Configuration and change management
  • 3. AI system development and deployment
- 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 (Q205-Q210):

NEW QUESTION # 205
What is the PRIMARY purpose of a model inventory in AI governance?

Answer: C

Explanation:
A model inventory (or AI system registry) is foundational to governance - without it, organizations cannot apply risk-tiered oversight, track model versions, or respond effectively to incidents.


NEW QUESTION # 206
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

Answer: D

Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Project Lifecycle Alignment"


NEW QUESTION # 207
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?

Answer: C


NEW QUESTION # 208
The PRIMARY purpose of utilizing neural networks in AI is to:

Answer: D


NEW QUESTION # 209
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

Answer: A

Explanation:
Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIATM Study Guide states,
"Traceability and documentation of all changes--including retraining events, parameter adjustments, and update rationales--enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.


NEW QUESTION # 210
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