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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - AI Audit Outputs and Reporting - Audit Planning and Design - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques |
| Topic 2: AI Operations | 46% | - Third-Party and Supply Chain Risk - Operational Controls and Readiness - Incident Management and Resilience - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment |
| Topic 3: AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Risk Management |
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NEW QUESTION # 33
An IS auditor is considering the integration of AI techniques into the audit sampling process. Which of the following BEST enables the auditor to identify high-risk transactions within large data sets for targeted sampling?
Answer: C
Explanation:
Predictive analyticsis the most effective method for identifyinghigh-risk transactionsbecause it uses statistical models, anomaly detection, and machine learning to:
* Rank transactions by inherent and residual risk
* Detect hidden patterns that auditors cannot manually identify
* Highlight unusual transaction profiles, outliers, and red flags
* Prioritize transactions that require deeper inspection
AAIA's audit domain emphasizesrisk-based sampling enhanced by AI, where predictive models significantly improve coverage and accuracy.
NLP (A) extracts insights from text-not ideal for transaction risk scoring.
OCR (B) digitizes documents but does not identify risk.
Rule-based analytics (C) only catches known patterns; predictive analytics uncoversunknown or emerging risks.
References:
AAIA Domain 3: AI in Audit Processes(advanced analytics, anomaly detection, risk scoring).
NEW QUESTION # 34
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?
Answer: D
Explanation:
Cross-validation testing is a statistical method used to assess how well a model generalizes to an independent data set. The AAIATM Study Guide recommends this method as a best practice to fine-tune model accuracy and reduce both false positives and false negatives. It involves splitting the dataset into training and testing subsets multiple times to ensure model robustness.
"Cross-validation allows auditors and developers to identify overfitting and adjust model parameters to achieve better generalization and predictive accuracy, especially in fraud detection contexts."
NEW QUESTION # 35
During an audit of a bank ' s AI credit scoring system, an IS auditor discovers that applicants were not informed about automated decision-making. Which of the following should the auditor do FIRST?
Answer: B
Explanation:
Transparency is a fundamental legal and ethical requirement for AI systems, particularly under regulations like GDPR, which mandate that data subjects be informed of automated decision-making. If an auditor finds that applicants were not informed, the immediate " First " step is to " Evaluate transparency controls " to determine why the notification process failed and to assess the scope of the non-compliance. This includes reviewing user agreements, privacy notices, and communication procedures. Once the failure is understood and the risk assessed, the auditor can move on to evaluating the appeal process (Option C) or preparing the final report (Option B).
NEW QUESTION # 36
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?
Answer: B
Explanation:
Bias in AI models is most commonly introduced through the training data. The AAIA™ Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results." While adaptability (C) and model parameters like temperature (D) affect behavior, they do not address the root cause of most biases. The development environment (B) supports infrastructure but not ethical assurance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias and Fairness in AI Systems"
NEW QUESTION # 37
Which of the following is the BEST approach to mitigate the risk of " AI model degradation " ?
Answer: A
Explanation:
Model degradation occurs as the " Freshness " of the training data wanes and real-world conditions evolve.
The most robust control is " Periodic human reviews " (Human-in-the-Loop). Human experts can identify " drift " in logic or common-sense failures that automated systems might miss. Relying on model-generated data (Option A) can lead to " Model Collapse, " where the AI begins to drift into nonsensical patterns by reinforcing its own previous outputs. Human oversight ensures the model remains grounded in reality and aligned with business objectives.
NEW QUESTION # 38
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