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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 (Q89-Q94):

NEW QUESTION # 89
An organization uses an AI-powered tool to detect and respond to cybersecurity threats in real time. An IS auditor finds that the tool produces excessive false positives, increasing the workload of the security team. Which of the following techniques should the auditor recommend to BEST evaluate the tool's effectiveness in managing this issue?

A. Conduct penetration testing to assess the system's ability to detect genuine threats.
B. Deploy a machine learning (ML) validation tool to increase the model's accuracy and performance.
C. Implement a benchmarking tool to compare the system's alerting capability with industry standards.
D. Use a log analysis tool to examine the types and frequency of alerts generated.

Answer: B

Explanation:
The AAIATM Study Guide recommends using validation tools to fine-tune and evaluate ML models, particularly when high false positives undermine operational efficiency. ML validation can identify threshold adjustments, retraining needs, or feature misweighting contributing to excessive alerting.
"Model validation enables organizations to quantify performance, reduce false alarms, and recalibrate AI behavior to align with operational needs and threat landscapes."

NEW QUESTION # 90
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

A. Identifying separation of duties conflicts for database data changes
B. Summarizing meeting transcripts from interviews with database administrators (DBAs)
C. Identifying technology-specific risk and considerations
D. Developing architecture diagrams

Answer: C

NEW QUESTION # 91
An IS auditor is assessing the implementation of AI tools for evidence collection involving multiple data sources. Which of the following outcomes BEST indicates that AI-driven evidence collection has improved the audit process?

A. Elimination of human judgment in data and evidence analysis
B. Reduced time spent gathering data with fewer errors in evidence compilation
C. Extended reporting timelines that allow for AI model retraining
D. Ability to rely on unstructured data with minimal cleansing

Answer: B

Explanation:
AI-driven evidence collection should enhanceefficiency and accuracy. The BEST indicator of improvement isreduced time spent gathering data with fewer errors(B), showing that AI has streamlined data extraction, consolidation, and initial validation without compromising quality. AAIA's content on AI in audit processes highlights benefits such asautomation of repetitive tasks, improved coverage, and higher-quality evidence.
Extended timelines for retraining (A) suggest inefficiency rather than improvement. Eliminating human judgment (C) is neither realistic nor recommended; professional skepticism and auditor judgment remain essential. Relying on unstructured data with minimal cleansing (D) can increase risk of misinterpretation or noise. Therefore, more efficient and accurateevidence compilationis the clearest positive outcome.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (efficiency and quality improvements in audit).
ISACA internal audit and data analytics guidance on AI's value in evidence collection.

NEW QUESTION # 92
Which of the following techniques is BEST to use when there is a limited dataset of detailed images available to train a convolutional neural network (CNN) model?

A. Minority class undersampling
B. Splitting the dataset 50/50 between training and testing sets
C. Duplication of current dataset
D. Transfer learning from an open-source model

Answer: D

Explanation:
When data is scarce, training a deep CNN from scratch is likely to result in overfitting. " Transfer learning " allows an organization to take a model previously trained on a massive dataset (like ImageNet) and " fine-tune
" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits. Duplicating data (Option D) does not add new information and only leads to overfitting.

NEW QUESTION # 93
An IS auditor notes that an AI model achieved significantly better results on training data than on test dat a. Which of the following problems with the model has the IS auditor identified?

A. Bias
B. Overfitting
C. Underfitting
D. Generalization

Answer: B

NEW QUESTION # 94
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