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[Hardware] AAIA Exam Details & AAIA Valid Test Tips

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【Hardware】 AAIA Exam Details & AAIA Valid Test Tips

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ISACA Advanced in AI Audit Sample Questions (Q136-Q141):NEW QUESTION # 136
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?
  • A. Determine whether the information is reliable.
  • B. Validate that the tool is leveraging the latest model.
  • C. Validate that the tool provides a privacy notice.
  • D. Determine whether any AI model hallucinations have occurred.
Answer: A
Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIA™ Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies." While hallucinations (C) and privacy notices (B) are important concerns, the primary auditor responsibility is to ensure that source data is accurate and suitable. Therefore, D is the correct response.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Auditor Responsibility and AI Input Validation"

NEW QUESTION # 137
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?
  • A. Retrain the model immediately and implement privacy-preserving techniques.
  • B. Limit the model's outputs to anonymized results while investigating further.
  • C. Audit the data pipelines of all partners to identify the source of the leak.
  • D. Disable the shared model and notify partners of the potential breach.
Answer: D
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 AAIA™ 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." While options A and D are longer-term remediation steps and B is investigative, none of them provide the urgent containment that is best practice in such a breach context.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "AI Data Breach and Disclosure Management"

NEW QUESTION # 138
Which of the following is the PRIMARY advantage of using K-fold cross validation when evaluating the performance of a machine learning (ML) model?
  • A. It helps minimize computational costs when evaluating complex models.
  • B. It enables the reduction of model bias by setting the K variable to higher values.
  • C. It facilitates performing regressions on smaller data sets.
  • D. It uses multiple training and testing cycles to minimize overfitting.
Answer: D
Explanation:
The primary advantage ofK-fold cross validationis that it uses multiple train/test splits, cycling through all folds so that each observation is used both for training and testing at different points. This process provides a more reliable estimate of model performance andreduces the risk of overfitting to a single split(option D).
It is an established best practice in model evaluation and aligns with AAIA's emphasis ontesting techniques for AI solutions and data analytics.
Option A is not specific to regressions; cross validation can be used for classification and other models as well. Option B can actually increase computational cost since multiple models are trained. Option C misunderstands bias-variance trade-offs; increasing K doesn't simply "reduce model bias." The key advantage remains the use of repeated, varied splits to better assess generalization and guard against overfitting.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Testing Techniques for AI Solutions; AI- specific testing).
ISACA data analytics content used in AAIA prep covering cross validation as a standard evaluation method.

NEW QUESTION # 139
Which of the following is the BEST use of AI to audit relationships for conflicts of interest or collusion?
  • A. Monte Carlo simulation
  • B. Correlation matrix
  • C. Time series analysis
  • D. Graph analytics
Answer: D
Explanation:
Graph analytics is specifically designed to analyze complex relationships among people, entities, transactions, and systems. According to AAIA audit methodologies, graph analytics helps identify hidden or non-obvious relationships indicative of:
* Collusion
* Fraud rings
* Undisclosed conflicts of interest
* Influence networks
* Hidden ownership structures
Correlation matrices (A) only measure linear relationships. Time series (B) identifies patterns over time, not relationships. Monte Carlo simulation (D) models uncertainty but does not uncover relational structures.
Graph analytics is the strongest AI-enabled method for mapping and auditing relational risks.
References:
AAIA Domain 3: AI Tools for Audit Analytics
AAIA Domain 4: Relationship Analysis and Fraud Detection

NEW QUESTION # 140
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:
  • A. Precision
  • B. Recall
  • C. Accuracy
  • D. Completeness
Answer: A
Explanation:
Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
"Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications." Accuracy and recall give broader insights, but only precision directly measures false positive risk.
Completeness is not a standard ML metric.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Performance Metrics for Classification Models"

NEW QUESTION # 141
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