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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q255-Q260):

NEW QUESTION # 255
When initiating an AI governance program, which of the following is MOST critical to ensure the AI system aligns with organizational objectives and stakeholder needs?

Answer: C

Explanation:
Effective AI governance begins with " Problem Framing " and process identification. Before acquiring hardware (Option B) or setting up monitoring (Option C), the organization must clearly define the specific business processes the AI will augment or automate. This ensures that the development of the model is rooted in strategic objectives rather than technological hype. The ISACA AAIA™ framework emphasizes that governance structures must first validate the " use case " to ensure it is ethical, feasible, and value-adding.
Without a well-defined use case, the organization risks implementing AI that is legally compliant (Option A) but operationally irrelevant or harmful to its core mission.


NEW QUESTION # 256
Which of the following BEST helps an organization manage bias in AI model decisions?

Answer: D

Explanation:
Bias is often subtle and context-dependent, making it difficult for automated systems to detect on their own. "Human oversight and feedback mechanisms" (Human-in-the-loop) allow domain experts to review model decisions and flag outcomes that appear discriminatory or unfair.
According to the AAIATM framework, these human feedback loops are critical for "correcting" the model's logic over time.


NEW QUESTION # 257
Which of the following is the MOST important reason to establish AI governance structures that extend beyond regulatory compliance?

Answer: B

Explanation:
While regulatory compliance is essential, AAIA underlines that ethical integrity must guide AI design, deployment, and monitoring. Regulations often lag behind technological capabilities; thus, relying solely on compliance leaves gaps in areas such as fairness, transparency, human dignity, and societal impact. The MOST important reason to extend governance structures beyond compliance is to ensure ethical integrity throughout the AI life cycle (C) -- from data collection and model design to deployment, monitoring, and retirement.


NEW QUESTION # 258
Which of the following is the MOST important step in an AI incident management process to ensure continuous improvement?

Answer: A

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 # 259
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?

Answer: D

Explanation:
Class imbalance occurs when one or more classes are underrepresented in the training data. The GREATEST concern is model bias (C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate into systematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).


NEW QUESTION # 260
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