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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 (Q327-Q332):

NEW QUESTION # 327
Which of the following is the GREATEST benefit of integrating AI in ransomware prevention?

Answer: C

Explanation:
Ransomware often involves subtle, unusual patterns of data access and encryption activity. The primary advantage of AI is its ability to "Analyze large volumes of data in real-time" to identify behavioral anomalies that traditional signature-based security tools might miss. AI can detect
"Zero-Day" ransomware by recognizing the intent of malicious actions (such as rapid file renaming or unauthorized encryption). This proactive detection is critical for stopping an attack before data exfiltration or total system lockout occurs.


NEW QUESTION # 328
An IS auditor reviewing documentation for an AI model notes that the modeler utilized a K-means clustering algorithm, which clusters data into categories for correlations and analysis. Which of the following is the MOST important risk for the auditor to consider?

Answer: A


NEW QUESTION # 329
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?

Answer: C

Explanation:
When a dataset lacks sufficient examples of a " minority class " (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to " Augment the data through synthesizing " (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain.
Relabeling (Option D) does not solve the underlying scarcity of the data needed for diverse representation.


NEW QUESTION # 330
Which control BEST addresses the risk of "model extraction" attacks, where an attacker queries an AI system to reverse-engineer its logic?

Answer: A

Explanation:
Model extraction attacks rely on repeated, systematic querying. Rate limiting and anomaly detection on API usage patterns reduce the attacker's ability to reconstruct the model.


NEW QUESTION # 331
The PRIMARY purpose of maintaining an audit trail in AI systems is to:

Answer: B


NEW QUESTION # 332
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