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ISACA AAIA Exam Syllabus Topics:

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

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ISACA Advanced in AI Audit Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which of the following is the MOST important consideration when auditing the data used for training an AI model?

Answer: C

Explanation:
Representativeness ensures that the training data reflects the full spectrum of conditions the AI model will encounter in production. According to the AAIATM Study Guide, models trained on non-representative data are prone to bias, poor generalization, and underperformance in real- world applications.
"Ensuring that training data accurately represents the operational environment is critical for model reliability, fairness, and scalability. Without it, the model may perform well in testing but fail in actual usage."


NEW QUESTION # 18
A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?

Answer: D

Explanation:
Data drift occurs when the statistical properties of input data change over time, impacting the accuracy of an AI model. In this case, the model failed to adapt to recent demand trends, indicating that the data on which it was trained no longer reflects current behavior.
"Data drift leads to performance degradation in AI systems when real-world input data shifts away from training data patterns. Monitoring for drift is essential, particularly in dynamic environments such as retail."


NEW QUESTION # 19
A bank is deploying an AI model trained on customer transactions in a multi-cloud environment. Which of the following BEST supports regulatory compliance regarding data security and privacy?

Answer: D

Explanation:
In a multi-cloud environment, data often traverses various geographical regions. For financial institutions, " Data residency and sovereignty " are the most critical compliance factors, ensuring that data is stored and processed within the jurisdictions required by law (e.g., GDPR or local banking acts). Failure to control where data resides can lead to severe legal penalties. While " Privacy by Design " and " DPIA " are essential methodologies, they do not specifically address the physical and legal location of data across cloud providers.
Sovereignty controls ensure the bank maintains control over its data assets and complies with specific cross- border data transfer regulations.


NEW QUESTION # 20
Which of the following is the GREATEST risk associated with using AI in audit planning?

Answer: B

Explanation:
In the context of AI-driven audit planning, the AAIA™ Study Guide identifies incomplete or inaccurate data as the most significant risk. If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.
"Audit planning supported by AI tools is only as strong as the underlying data. Incomplete data may cause incorrect risk assessments or misallocate audit resources, undermining audit effectiveness." While scope creep and cost are common concerns in project management, and limited AI knowledge can be mitigated through training, only incomplete data directly impacts the validity of audit planning outputs.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Data Integrity and Planning Risks"


NEW QUESTION # 21
Which of the following is the MOST important step in an AI incident management process to ensure continuous improvement?

Answer: D

Explanation:
Root cause analysis (option B) is the most critical step for continuous improvement because it ensures that incidents are not only resolved but prevented from recurring.
AAIA incident management emphasizes:
* Identifying underlying systemic failures
* Determining whether issues arose from data, model logic, drift, integration, or human factors
* Implementing long-term mitigation strategies
* Updating governance and operational controls
Defining ownership (A) is important early in the process.
Assessing severity (D) prioritizes response.
Archiving logs (C) supports documentation.
But none of these guarantee process learning or long-term resilience.
Root cause analysis is the essential step for organizational maturation and risk reduction.
References:
AAIA Domain 2: Incident Management and Post-Incident Review
AAIA Domain 1: Continuous Improvement in AI Governance


NEW QUESTION # 22
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