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ISACA AAIA Exam Syllabus Topics:

SectionObjectives
AI Governance and Risk- AI Security and Controls
  • 1. Identity and access management
  • 2. Vendor and supply chain risk
  • 3. Threat and vulnerability management
- AI Governance Frameworks
  • 1. AI risk management
  • 2. AI policies and governance structures
  • 3. Regulatory and compliance considerations
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
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. Operational AI controls
  • 3. Data quality and integrity

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ISACA Advanced in AI Audit Sample Questions (Q221-Q226):

NEW QUESTION # 221
An IS auditor notes that an AI modelachieved significantly better results on training data than on test data.
After the development, a quality assurance (QA) team checks that all input variables and parameters match the technical design. Which of the following BEST describes this activity?

Answer: D

Explanation:
The ISACA AAIA™ framework distinguishes between " Verification " and " Validation " . " Verification " is a QA process that checks if the system was " built right " -i.e., whether the code, parameters, and architecture match the design documentation. " Validation " (Option A) checks if the " right system " was built-i.e., whether the model actually performs correctly on unseen data and meets business goals. While testing (Option C) identifies the performance gap, the specific act of auditing the model ' s configuration against its technical blueprints is a verification activity.


NEW QUESTION # 222
During a risk assessment for an AI system, data drift was identified as a key risk. Which of the following is the BEST course of action?

Answer: D

Explanation:
Data drift occurs when the statistical properties of input data change over time, causing the AI model to produce increasingly inaccurate or biased results.
The correct response is to document the risk and implement continuous monitoring (A) because:
Drift requires ongoing detection, not a one-time fix
Retraining too early can reinforce issues (especially with the same dataset)


NEW QUESTION # 223
When developing an audit plan, which of the following is MOST important specifically for the transparency of an AI application?

Answer: B

Explanation:
Explainability testing is essential for determining how and why an AI model produces specific outputs. Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
Stakeholders understand model decisions
Auditors can verify fairness and accuracy
Regulators can review decision logic
The organization can justify outcomes to affected individuals
Models align with ethical and governance expectations


NEW QUESTION # 224
An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?

Answer: D

Explanation:
Generative AI tools such as large language models often have token limitations that restrict how much input they can process in a single prompt. According to the AAIATM Study Guide, when input exceeds token limits, the model processes only the initial portion--leading to biased outputs based on early entries.
"Token limits in generative AI systems may lead to partial input processing, skewing model outputs toward the beginning of the data and introducing interpretive bias. Auditors must assess whether output reflects the full dataset."


NEW QUESTION # 225
An organization shares an AI model with external partners. One partner reports that sensitive data has been inadvertently exposed through the model's outputs. Which of the following is the IS auditor's BEST recommendation?

Answer: B

Explanation:
In the case of a potential data exposure through AI model outputs, the first and most responsible action from an auditing and risk standpoint is to halt further risk propagation. According to the AAIATM Study Guide, immediate containment is vital, especially when regulatory and reputational risks are high.
"Upon detection of a data breach risk, AI models should be immediately disabled from public or partner use, and all relevant parties should be notified as part of a responsible disclosure and containment strategy."


NEW QUESTION # 226
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