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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 46% | - Operational Controls and Readiness - Performance Monitoring and Evaluation - Third-Party and Supply Chain Risk - Incident Management and Resilience - AI System Lifecycle and Deployment |
| AI Governance and Risk | 33% | - AI Risk Management - Privacy and Data Governance Programs - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - AI Governance and Program Management |
| AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Audit Evidence Collection Techniques - Audit Testing and Sampling Methodologies - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting |
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NEW QUESTION # 77
Which of the following should be applied to an AI system but are not typically used in traditional systems?
Answer: A
Explanation:
AI systems face unique threats not commonly found in traditional IT environments, particularly data poisoning, where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning--such as input provenance checks, anomaly detection on training data, and integrity validation pipelines--are emphasized in AAIA's coverage of AI- specific vulnerabilities.
NEW QUESTION # 78
Which of the following is the BEST way to ensure an AI model ' s outputs are effectively controlled for bias?
Answer: C
Explanation:
The most objective evidence of bias control is " Performance Parity " . If the accuracy, precision, and recall are " Similar across various demographic groups, " it demonstrates that the model is functioning equitably for all users. While having a policy definition (Option B) is good governance, it doesn ' t prove the model is fair in practice. Restricting training to " Real-world human decisions " (Option C) often introduces bias, as it simply automates historical human discrimination. Transparency (Option D) is necessary for auditing but does not, on its own, prevent a model from being biased.
NEW QUESTION # 79
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?
Answer: D
Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIATM Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.
NEW QUESTION # 80
An IS auditor notes that an AI model achieved significantly better results on training data than on test data. Which of the following problems with the model has the IS auditor identified?
Answer: C
Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIATM Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs."
NEW QUESTION # 81
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
Answer: B
Explanation:
Imputing missing values using the mean, median, or mode is a common technique to fill blanks in datasets.
However, according to the AAIA™ Study Guide, this method introduces a significant risk of information distortion, especially if the data is not normally distributed or if imputed values disproportionately impact underrepresented groups.
"Simple imputation can reduce data variability and reinforce existing biases. It may also misrepresent the true distribution of the data, leading to skewed model outputs." Other options (B, C, D) may involve transformations, but they do not inherently cause as much bias or data misrepresentation as A.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Imputation and Transformation Risks"
NEW QUESTION # 82
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