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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q319-Q324):

NEW QUESTION # 319
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

Answer: D

Explanation:
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing. According to the AAIA™ Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made-false positives and false negatives-thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed-not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Evaluation Metrics"


NEW QUESTION # 320
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

Answer: D


NEW QUESTION # 321
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

Answer: C

Explanation:
Adversarial attacks involve " manipulated inputs " (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must " Validate the process for handling manipulated inputs. " This includes checking for robust input validation, anomaly detection on incoming data, and " adversarial training " where the model is intentionally exposed to these attacks during development to build resilience. Focusing on logs or speed (Options A, B, and C) does not address the fundamental vulnerability of the algorithm to technical manipulation.


NEW QUESTION # 322
Which of the following is the MOST important consideration for change management related to the organization-wide adoption of AI systems and tools?

Answer: C

Explanation:
For organization-wide adoption of AI, the MOST important change management consideration is that the organization has suitable data governance and infrastructure readiness (D). Without robust data governance, AI systems may rely on poor-quality, noncompliant, or insecure data; without appropriate infrastructure (compute, storage, monitoring, integration), implementations will be fragile, unreliable, and risky. AAIA stresses that AI readiness includes governance structures, data stewardship, and technical foundations.


NEW QUESTION # 323
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

Answer: D


NEW QUESTION # 324
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