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ISACA AAIA Exam Syllabus Topics:

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

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ISACA Advanced in AI Audit Sample Questions (Q312-Q317):

NEW QUESTION # 312
Which of the following would be the BEST time to create a data governance plan for a new AI solution?

Answer: A

Explanation:
In AI development, "Privacy by Design" and "Governance by Design" are fundamental principles.
The data governance plan should be created "As part of the initial design" phase. This ensures that the requirements for data quality, lineage, labeling, and privacy are integrated into the architecture from the beginning.


NEW QUESTION # 313
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?

Answer: B


NEW QUESTION # 314
When an auditor is using AI to test controls, what would be the HIGHEST risk to the audit ' s integrity?

Answer: D

Explanation:
The ISACA AAIA™ framework highlights that the use of AI in auditing does not relieve the auditor of their responsibility for professional judgment. The highest risk is " Over-reliance " or " Automation Bias, " where the auditor accepts AI-generated conclusions without independent validation. If the AI makes a false conclusion due to a hallucination or biased logic, and the auditor fails to " check under the hood, " the entire audit report becomes unreliable. While data completeness (Option C) and formatting (Option B) are important, the human-in-the-loop validation of AI outputs is the primary safeguard ensuring audit quality and accountability.


NEW QUESTION # 315
Which of the following is the MOST important purpose of conducting a risk assessment for AI models within an organization?

Answer: D

Explanation:
Risk assessments identify potential threats and vulnerabilities in AI systems and support the development of mitigation strategies. According to the AAIA™ Study Guide, the main objective is not just identification of risks but to proactively design controls that minimize impact and ensure ethical and regulatory compliance.
"The output of an AI risk assessment should lead to actionable mitigation strategies, supporting the secure and responsible deployment of AI solutions across business processes." Options A, C, and D are components of governance and monitoring, but B addresses the core intent of risk assessments.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Risk Assessment and Mitigation for AI Systems"


NEW QUESTION # 316
Which of the following is the BEST way to support the development and design of high-risk AI systems?

Answer: C

Explanation:
The AAIATM Study Guide emphasizes that the foundation of any high-performing and ethical AI system lies in the quality and integrity of its data. For high-risk AI systems, such as those used in healthcare, finance, or criminal justice, it is essential to base models on trustworthy data. This ensures reliable predictions, reduces bias, and mitigates risk.
"Trustworthy datasets are characterized by accuracy, completeness, consistency, and ethical sourcing. In high-risk AI applications, ensuring data quality at every stage is crucial to system reliability and compliance." While backups, user training, and MFA are important for security and operational resilience, they do not address the core challenge of ensuring model accuracy and fairness at the development and design phase. Therefore, option C is the most effective practice.


NEW QUESTION # 317
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