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| Section | Objectives |
|---|---|
| AI Auditing Tools and Techniques | - Audit Testing and Reporting
|
| AI Operations | - AI System Lifecycle
|
| AI Governance and Risk | - AI Security and Controls
|
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199. Frage
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?
Antwort: D
200. Frage
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations.
Which of the following MOST effectively detects data drift related to the model's predictions?
Antwort: D
Begründung:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time.
According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide,data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained.
If not detected, data drift can degrade model performance and lead to erroneous predictions.
Themost effective approach to detect data driftis tocontinuously compare the statistical distributions of incoming (production) data with those of the training dataset. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model's predictions may no longer be valid or optimal.
As stated in the AAIA™ Study Guide under "AI Model Monitoring and Maintenance":
"Monitoring input data for distributional changes compared to the model's training data is an essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs." While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness,they do not inherently detect data drift-they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and Maintenance," Subsection: "Detection and Management of Data Drift"
201. Frage
An organization deployed an AI-powered customer service chatbot trained using customer chat logs. During a risk assessment, which issue should be the IS auditor's GREATEST concern?
Antwort: A
Begründung:
The GREATEST concern is insufficient access controls (D), which can lead to unauthorized exposure of customer data--a severe privacy, security, regulatory, and reputational risk. Chat logs often contain personally identifiable information and sensitive communications. AAIA prioritizes data confidentiality, access control, and privacy obligations as highest-risk elements, particularly for customer-interactive AI systems.
202. Frage
Which of the following is the BEST indicator that an organization has mature MLOps practices?
Antwort: B
Begründung:
Mature MLOps integrates automated testing, validation gates, continuous monitoring, and rollback mechanisms into the deployment pipeline, mirroring DevOps discipline applied to machine learning.
203. Frage
An IS auditor is planning an audit of an AI medical prognosis system. Which of the following is the BEST way to test model transparency?
Antwort: C
Begründung:
SHAP (Shapley Additive Explanations) is the leading, industry-recognized method for explaining complex AI model predictions.
AAIA specifically identifies SHAP as a robust technique for transparency in high-impact systems such as medical AI.
SHAP provides:
Feature contribution breakdowns
Visual explanation of each decision
Global and local interpretability
Quantifiable insights into model logic
Compliance-friendly justification of predictions
204. Frage
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