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NEW QUESTION # 247
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 # 248
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 # 249
What is the MOST appropriate control to address the risk of an AI chatbot providing harmful medical advice to users?
Answer: C
Explanation:
Guardrails that restrict the chatbot's scope, combined with disclaimers and escalation paths to qualified professionals, mitigate the risk of harm from unqualified AI-generated medical guidance.
NEW QUESTION # 250
What is the PRIMARY purpose of a model inventory in AI governance?
Answer: C
Explanation:
A model inventory (or AI system registry) is foundational to governance - without it, organizations cannot apply risk-tiered oversight, track model versions, or respond effectively to incidents.
NEW QUESTION # 251
Which of the following is the GREATEST benefit of using AI to evaluate data quality during an audit?
Answer: A
Explanation:
Traditional data quality checks often rely on manual sampling, which can miss rare but significant errors. AI- driven audit tools can analyze 100% of a population to " identify outliers " that represent data errors, anomalies, or potential fraud. According to the AAIA™ framework, identifying these outliers is crucial because " dirty data " can fundamentally skew AI model predictions and audit conclusions. By automating the detection of data inconsistencies, auditors can focus their manual efforts on investigating high-risk items, thereby increasing both the accuracy of the audit and the overall reliability of the data used for model training.
NEW QUESTION # 252
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