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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Auditing Tools and Techniques | 21% | - Audit Evidence Collection Techniques - Audit Planning and Design - Audit Testing and Sampling Methodologies - Data Quality and Analytics for AI Audit - AI Audit Outputs and Reporting |
| Topic 2: AI Governance and Risk | 33% | - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - AI Risk Management |
| Topic 3: AI Operations | 46% | - Incident Management and Resilience - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment - Operational Controls and Readiness - Third-Party and Supply Chain Risk |
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NEW QUESTION # 108
Which of the following is MOST important to perform when testing an AI model for security?
Answer: A
Explanation:
AI security testing must address both traditional IT vulnerabilities and AI-specific threats.
According to the AAIATM framework, "Vulnerability Assessments" are essential for identifying weaknesses in the AI pipeline, including insecure APIs, lack of input sanitization, and susceptibility to adversarial attacks. This assessment helps auditors determine if the system can resist exploitation, such as prompt injection or data poisoning.
NEW QUESTION # 109
Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?
Answer: D
Explanation:
The primary goal of any AI system is to provide predictions or classifications that support business decisions.
The AAIA™ Study Guide highlights that model accuracy-especially when validated against actual outcomes-is the most reliable indicator of whether the AI supports organizational goals effectively.
"Accuracy, precision, and recall are foundational metrics that indicate whether a model is performing in line with its intended objectives. High user engagement or retraining frequency does not confirm effective decision support unless the outputs are correct." Cost and user numbers offer useful operational insights but do not reflect the alignment of AI performance with strategic goals. Thus, D is the most meaningful KPI in this context.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "AI Metrics and Business Alignment"
NEW QUESTION # 110
An IS auditor finds that an AI model's outputs are not being reviewed. Which of the following would BEST address this risk?
Answer: D
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 # 111
An IS auditor notes that an AI modelachieved significantly better results on training data than on test data.
After the development, a quality assurance (QA) team checks that all input variables and parameters match the technical design. Which of the following BEST describes this activity?
Answer: C
Explanation:
The ISACA AAIA™ framework distinguishes between " Verification " and " Validation " . " Verification " is a QA process that checks if the system was " built right " -i.e., whether the code, parameters, and architecture match the design documentation. " Validation " (Option A) checks if the " right system " was built-i.e., whether the model actually performs correctly on unseen data and meets business goals. While testing (Option C) identifies the performance gap, the specific act of auditing the model ' s configuration against its technical blueprints is a verification activity.
NEW QUESTION # 112
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.
NEW QUESTION # 113
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