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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operations | 46% | - Third-Party and Supply Chain Risk - AI System Lifecycle and Deployment - Operational Controls and Readiness - Performance Monitoring and Evaluation - Incident Management and Resilience |
| Topic 2: AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - AI Risk Management - Privacy and Data Governance Programs - Ethics, Regulations, and Standards for AI - AI Governance and Program Management |
| Topic 3: AI Auditing Tools and Techniques | 21% | - Data Quality and Analytics for AI Audit - Audit Planning and Design - Audit Testing and Sampling Methodologies - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques |
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NEW QUESTION # 86
An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?
Answer: B
Explanation:
In a proper AI pipeline, the original data must be "strictly partitioned" into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were "Reused" across sets.
This leads to "Data Leakage," where the model "sees" the test or validation data during the training phase. As a result, the model's performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIATM Study Guide identifies data leakage as a primary cause of "Model Evaluation Failure," rendering all testing results invalid.
NEW QUESTION # 87
An organization has deployed a generative AI system for customer support that includes frequent updates to the AI model after deployment. Which of the following represents the GREATEST risk?
Answer: C
Explanation:
When AI models are updated frequently in production, continuous monitoring is critical to detect performance degradation, bias drift, hallucinations, and security issues introduced by new versions. A lack of continuous monitoring (option C) means the organization might not promptly detect harmful behaviors or compliance violations, despite frequent changes, exposing it to operational, reputational, and regulatory risk.
NEW QUESTION # 88
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 AAIATM 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."
NEW QUESTION # 89
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
Answer: C
Explanation:
Representativeness means that training data accurately reflects thecurrent real-world environmentin which the AI system operates. The BEST way to ensure this is by verifying that thetraining data remains relevant and aligned with evolving real-world conditions(C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
Manual review (A) is limited in scope and scale. Automated validation (B) helps detect errors but does not ensure data reflects the real world. Synthetic data (D) supplements but does not guarantee representativeness unless calibrated properly. Therefore,continuous relevance and contextual alignmentis the most important factor.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data relevance, drift detection, representativeness).
NEW QUESTION # 90
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: A
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 # 91
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