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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Data Quality and Analytics for AI Audit - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Testing and Sampling Methodologies |
| Topic 2: AI Operations | 46% | - Third-Party and Supply Chain Risk - Incident Management and Resilience - Operational Controls and Readiness - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment |
| Topic 3: AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Governance and Program Management - AI Models, Considerations, and Requirements - AI Risk Management |
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NEW QUESTION # 313
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?
Answer: A
Explanation:
Excessive agencyoccurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is tominimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance onlimiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open- source extensions (D) improve transparency but do not inherently control excessive agency.Scope limitation is the most targeted and effective mitigation.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Governance, AI Autonomy, and Risk Controls.
NEW QUESTION # 314
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?
Answer: A
Explanation:
Unchecked AI outputs pose a significant risk because incorrect or biased results may propagate into decision- making processes. The AAIA™ Study Guide stresses that validating AI outputs for consistency, accuracy, and reasonableness is a critical responsibility of both operators and auditors.
"Auditors must verify that appropriate output validation mechanisms are in place. Failure to check outputs increases the likelihood of undetected errors influencing downstream processes or decisions." While cascading failures (C) are a consequence of unchecked outputs, B is the root issue. A and D present logistical and documentation risks but not the highest immediate impact.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Audit Validation of AI Outputs"
NEW QUESTION # 315
Which of the following presents the GREATEST risk when an organization deploys a machine learning model in a public cloud environment for real-time predictions?
Answer: A
Explanation:
In a real-time prediction environment (e.g., fraud detection, medical triage, automotive risk), latency and inference speed directly affect safety, accuracy, and business performance.
If the SLA does not include guarantees for latency, the model may fail to deliver predictions in time, leading to:
Incorrect or delayed decisions
Transaction failures
Safety incidents in time-sensitive use cases
Compliance violations in regulated domains
NEW QUESTION # 316
Which of the following is the BEST way to validate that an AI model's documented limitations match its actual behavior in production?
Answer: C
Explanation:
Ongoing production monitoring data provides objective evidence of real-world behavior, which should be reconciled against documented assumptions and limitations to detect scope creep or undisclosed use cases.
NEW QUESTION # 317
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?
Answer: B
Explanation:
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing.
According to the AAIATM Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made--false positives and false negatives--thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed--not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
NEW QUESTION # 318
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