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| Section | Objectives |
|---|---|
| AI Operations | - AI System Lifecycle
|
| AI Auditing Tools and Techniques | - Audit Testing and Reporting
|
| AI Governance and Risk | - AI Governance Frameworks
|
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NEW QUESTION # 207
Which of the following is the MOST important step in an AI incident management process to ensure continuous improvement?
Answer: A
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
NEW QUESTION # 208
Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?
Answer: A
Explanation:
The primary goal of any AI system is to provide predictions or classifications that support business decisions.
The AAIA™ Study Guide highlights that model accuracy-especially when validated against actual outcomes-is the most reliable indicator of whether the AI supports organizational goals effectively.
"Accuracy, precision, and recall are foundational metrics that indicate whether a model is performing in line with its intended objectives. High user engagement or retraining frequency does not confirm effective decision support unless the outputs are correct." Cost and user numbers offer useful operational insights but do not reflect the alignment of AI performance with strategic goals. Thus, D is the most meaningful KPI in this context.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "AI Metrics and Business Alignment"
NEW QUESTION # 209
Which of the following is the BEST way to ensure data fed into an AI model aligns with business objectives?
Answer: C
Explanation:
Documenting data input requirements (option C) ensures that all incoming data supports the business purpose, operational constraints, and intended use cases of the AI model.
AAIA highlights that aligning AI systems with business objectives starts withclear data specifications, including:
* Required fields and data formats
* Data quality thresholds
* Acceptable ranges and constraints
* Mandatory attributes
* Source system definitions
* Business rationale for each feature
Without documentation, data pipelines may ingest irrelevant, low-quality, or misaligned data, causing the model to drift away from business needs.
Normalization (A) improves preprocessing but does not ensure alignment.
Switching data sources (B) is premature without evaluating needs.
Defining new attributes (D) is secondary to documenting overall requirements.
References:
AAIA Domain 1: Business Alignment and Data Requirements
AAIA Domain 2: Input Specification Governance
NEW QUESTION # 210
Which of the following correctly summarizes the conclusions of the model card excerpt provided?
Model Card - Electrical Grid Predictive Maintenance Model
Model Information:
* Description: AI model designed to predict maintenance needs for electrical grid components, reduce unplanned downtime, and improve grid reliability.
* Inputs: Real-time sensor data, historical maintenance records, and operational logs.
* Outputs: Maintenance needs predictions for 60 & 90 days.Evaluation:
* Approach: Cross-validation and validation of accuracy, precision, and recall.
* Results: Accuracy 72%; Precision 60%; Recall 95%; F1 76%
Answer: B
Explanation:
The F1 score is the harmonic mean of precision and recall, offering a balanced measure of model accuracy, especially when there is an imbalance in the classes (e.g., more "no-maintenance" than "needs-maintenance" outcomes). According to the AAIA™ Study Guide, the F1 score is used to evaluate how well the model identifies true positives while balancing the risk of false positives and false negatives.
"An F1 score summarizes the model's ability to correctly identify relevant events-in this case, true maintenance needs. A 76% F1 score means the model is relatively balanced and effective at catching maintenance requirements without generating too many false alerts." Thus, D is correct. Option A misrepresents recall as predictive accuracy. Option B misinterprets accuracy.
Option C has no direct basis in the excerpt.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Understanding Evaluation Metrics and Model Cards"
NEW QUESTION # 211
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?
Answer: D
Explanation:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIA™ Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved." While open-source usage (C) is a concern, only B provides legal clarity. Metrics (A) and logs (D) reflect performance-not legal compliance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Rights, Licensing, and Intellectual Property"
NEW QUESTION # 212
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