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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 46% | - Operational Controls and Readiness - Incident Management and Resilience - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment - Third-Party and Supply Chain Risk |
| AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Risk Management - AI Governance and Program Management - AI Models, Considerations, and Requirements |
| AI Auditing Tools and Techniques | 21% | - Audit Testing and Sampling Methodologies - Audit Planning and Design - Audit Evidence Collection Techniques - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting |
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NEW QUESTION # 138
Which of the following is the MOST appropriate control for ensuring accountability when an AI system makes an erroneous high-impact decision?
Answer: C
Explanation:
Accountability requires a defined process -- someone must be responsible for reviewing, explaining, and remediating erroneous high-impact AI decisions; disclaimers alone do not constitute a control.
NEW QUESTION # 139
An organization is implementing an AI governance framework. Which of the following should be established FIRST?
Answer: A
Explanation:
Governance frameworks begin with defining who is accountable for AI outcomes (business owner, model owner, risk owner). Without clear ownership, monitoring, incident response, and vendor management all lack a decision-making anchor.
NEW QUESTION # 140
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?
Answer: B
Explanation:
WhenAI outputs are not being reviewed, the primary concern is thatincorrect, biased, or non-compliant decisionsmay go undetected. The best way to address this risk is to implement avalidation process for AI decisions(B), which may include human-in-the-loop checks, sampling-based reviews, escalation criteria, and formal approval workflows. AAIA emphasizes the importance ofsupervision and validation of AI outputs, particularly where decisions have financial, legal, or safety implications.
A larger training dataset (A) does not guarantee correctness or fairness and does not replace oversight.
Regular retraining (C) can be beneficial but can also propagate errors if not validated. Prompt templates (D) are relevant mainly for generative and prompt-based systems, and they do not constitute systematic review of outputs. Therefore, astructured validation processis the most direct and effective control.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions).
ISACA audit guidance on AI decision validation and human oversight.
NEW QUESTION # 141
An organization uses an AI-powered tool to detect and respond to cybersecurity threats in real time. An IS auditor finds that the tool produces excessive false positives, increasing the workload of the security team. Which of the following techniques should the auditor recommend to BEST evaluate the tool's effectiveness in managing this issue?
Answer: C
Explanation:
The AAIATM Study Guide recommends using validation tools to fine-tune and evaluate ML models, particularly when high false positives undermine operational efficiency. ML validation can identify threshold adjustments, retraining needs, or feature misweighting contributing to excessive alerting.
"Model validation enables organizations to quantify performance, reduce false alarms, and recalibrate AI behavior to align with operational needs and threat landscapes."
NEW QUESTION # 142
Which of the following techniques BEST assesses an AI model's ability to generalize to new data?
Answer: A
Explanation:
K-fold cross validation partitions the dataset into k equally sized folds, iteratively using one fold for testing and the remaining folds for training. By rotating through all folds, the model is evaluated on multiple, non-overlapping subsets of data, providing a robust estimate of generalization performance. This process reduces the risk of overfitting to a single train-test split and is explicitly recognized in AI testing techniques and data analytics practices relevant to AAIA.
NEW QUESTION # 143
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