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ISACA AAIA Exam Syllabus Topics:
Topic
Details
Topic 1
- 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.
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
- 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.
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ISACA Advanced in AI Audit Sample Questions (Q14-Q19):
NEW QUESTION # 14
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?
- A. Cascading failures of AI system outputs
- B. Inability to identify where an AI system is housed
- C. System output not being checked for inconsistencies
- D. Difficulty of documenting AI algorithm processes
Answer: C
NEW QUESTION # 15
A car manufacturer uses an AI model to predict maintenance needs for its vehicles. Which of the following techniques can an IS auditor apply to MOST effectively verify the AI model's decisions to stakeholders?
- A. Using K-means algorithms to group vehicles based on mileage or engine temperature for maintenance patterns
- B. Using neural network visualization to show how the AI model processes data through its layers
- C. Utilizing support vector machines (SVM) to classify vehicles based on maintenance urgency
- D. Using local interpretable model-agnostic explanation (LIME) to analyze how specific features contribute to predictions
Answer: D
NEW QUESTION # 16
Which of the following is the BEST way to support the development and design of high-risk AI systems?
- A. Conduct regular training sessions for users on data privacy.
- B. Ensure the availability of trustworthy data sets.
- C. Implement multi-factor authentication (MFA) for all users accessing the AI system.
- D. Regularly back up the AI system's data to a secure, offsite location.
Answer: B
NEW QUESTION # 17
Which of the following is MOST important to have in place when initially populating data into a data frame for an AI model?
- A. An approved risk assessment for including, excluding, or subsequently dropping data attributes from the model
- B. An analysis of exploratory data that checks for incorrect data types, null values, and duplicate entries
- C. The code for separating data into training and testing data sets
- D. The box charts, histograms, scatterplots, and Venn diagrams that identify correlations and outliers
Answer: B
NEW QUESTION # 18
Which of the following BEST detects model drift or unexpected changes in AI model outputs?
- A. Anomaly monitoring
- B. AI model retraining
- C. Standardization of AI configurations
- D. AI model documentation reviews
Answer: A
NEW QUESTION # 19
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