AAIA Test Engine Preparation: ISACA Advanced in AI Audit - AAIA Study Guide - PDF4Test

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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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 (Q215-Q220):

NEW QUESTION # 215
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

Answer: C

Explanation:
Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIATM Study Guide states,
"Traceability and documentation of all changes--including retraining events, parameter adjustments, and update rationales--enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.


NEW QUESTION # 216
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?

Answer: B

Explanation:
The AAIA™ Study Guide highlights that generative AI tools can significantly enhance audit productivity, especially in content generation tasks. Drafting executive summaries-condensing complex findings into clear, concise formats-is a high-impact use case that benefits from generative AI's natural language generation capabilities.
"Generative AI can be leveraged by auditors to automate reporting sections, such as executive summaries, ensuring consistency and saving time without compromising content clarity." While A, C, and D are valid uses, B delivers the most value by expediting a time-intensive and high-level reporting task in complex audits.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Generative AI Applications in Audit Reporting"


NEW QUESTION # 217
An IS auditor analyzed an AI model scorecard and identified that training data was imbalanced. Which of the following is the BEST recommendation to remediate risk?

Answer: C

Explanation:
Data imbalance (where one class significantly outweighs another) leads to biased models that perform poorly on minority classes. The ISACA AAIA™ manual suggests that " class-weighted loss " is a proactive technical control that penalizes the model more for misclassifying minority samples, effectively " balancing " the learning process. Stratified splitting ensures that every training and validation set maintains the same ratio of classes as the original data, preventing the model from failing to " see " minority cases during the testing phase. Tuning thresholds (Options B and C) happens after the fact, whereas weighting addresses the root cause during training.


NEW QUESTION # 218
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

Answer: B


NEW QUESTION # 219
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

Answer: A


NEW QUESTION # 220
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