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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 46% | - Performance Monitoring and Evaluation - Incident Management and Resilience - Operational Controls and Readiness - Third-Party and Supply Chain Risk - AI System Lifecycle and Deployment |
| AI Governance and Risk | 33% | - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - AI Risk Management - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs |
| AI Auditing Tools and Techniques | 21% | - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Planning and Design - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies |
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NEW QUESTION # 47
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 # 48
Which of the following is the MOST effective control to prevent data poisoning attacks during model training?
Answer: A
Explanation:
Data poisoning involves injecting malicious or mislabeled data into the training set. Provenance verification and data validation pipelines reduce the likelihood of compromised data reaching the model.
NEW QUESTION # 49
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?
Answer: C
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 # 50
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?
Answer: A
Explanation:
Cross-validation testing is a statistical method used to assess how well a model generalizes to an independent data set. The AAIA™ Study Guide recommends this method as a best practice to fine-tune model accuracy and reduce both false positives and false negatives. It involves splitting the dataset into training and testing subsets multiple times to ensure model robustness.
"Cross-validation allows auditors and developers to identify overfitting and adjust model parameters to achieve better generalization and predictive accuracy, especially in fraud detection contexts." Regression testing (A) focuses on changes over time; substantive testing (C) is audit-specific but not model- focused. Benford's Law (D) applies to numerical distributions but is not designed for optimizing ML models.
Hence, B is the best approach.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Testing and Model Validation Methods"
NEW QUESTION # 51
Which of the following is the BEST recommendation for an organization that has adopted "vibe coding" (using AI to generate code based on high-level natural language prompts)?
Answer: C
Explanation:
"Vibe coding" or AI-assisted development carries the risk of introducing "hallucinated" vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led "security checklist" for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials.
NEW QUESTION # 52
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