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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
Topic 2	AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 3	AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.

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ISACA Advanced in AI Audit Sample Questions (Q15-Q20):

NEW QUESTION # 15
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

A. Completeness
B. Accuracy
C. Recall
D. Precision

Answer: D

Explanation:
Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIATM Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
"Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications." Accuracy and recall give broader insights, but only precision directly measures false positive risk.
Completeness is not a standard ML metric.

NEW QUESTION # 16
An IS auditor is considering the integration of AI techniques into the audit sampling process.
Which of the following BEST enables the auditor to identify high-risk transactions within large data sets for targeted sampling?

A. Natural language processing (NLP)
B. Optical character recognition (OCR)
C. Predictive analytics
D. Rule-based analytics

Answer: C

Explanation:
Predictive analytics is the most effective method for identifying high-risk transactions because it uses statistical models, anomaly detection, and machine learning to:
Rank transactions by inherent and residual risk
Detect hidden patterns that auditors cannot manually identify
Highlight unusual transaction profiles, outliers, and red flags
Prioritize transactions that require deeper inspection
AAIA's audit domain emphasizes risk-based sampling enhanced by AI, where predictive models significantly improve coverage and accuracy.

NEW QUESTION # 17
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

A. AI model vulnerability assessment
B. AI change management requests
C. AI model inventory
D. AI acceptable use policy

Answer: C

NEW QUESTION # 18
A manufacturing company installs an AI system to control robotic arms on its assembly line. The system learns over time, adjusting its movements based on production results to improve accuracy. What type of learning is the robot MOST likely using?

A. Reinforcement learning
B. Deep learning
C. Supervised learning
D. Unsupervised learning

Answer: A

Explanation:
Reinforcement Learning (RL) is characterized by an "agent" (the robot) that learns to achieve a goal in a dynamic environment through trial and error. It receives "rewards" for correct movements and "penalties" for errors, allowing it to optimize its "Policy Function" over time.

NEW QUESTION # 19
Which of the following is the BEST use of AI to audit relationships for conflicts of interest or collusion?

A. Monte Carlo simulation
B. Correlation matrix
C. Graph analytics
D. Time series analysis

Answer: C

Explanation:
Graph analytics is specifically designed to analyze complex relationships among people, entities, transactions, and systems. According to AAIA audit methodologies, graph analytics helps identify hidden or non-obvious relationships indicative of:
* Collusion
* Fraud rings
* Undisclosed conflicts of interest
* Influence networks
* Hidden ownership structures
Correlation matrices (A) only measure linear relationships. Time series (B) identifies patterns over time, not relationships. Monte Carlo simulation (D) models uncertainty but does not uncover relational structures.
Graph analytics is the strongest AI-enabled method for mapping and auditing relational risks.
References:
AAIA Domain 3: AI Tools for Audit Analytics
AAIA Domain 4: Relationship Analysis and Fraud Detection

NEW QUESTION # 20
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