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NEW QUESTION # 69
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?
Answer: A
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.
NEW QUESTION # 70
An IS auditor relies on generative AI to document audit narratives from walkthrough meetings.
The final version includes generic language and misses key exceptions. What safeguard could improve the reliability of AI-generated documents?
Answer: C
Explanation:
When generative AI produces generic or "vague" summaries, it is often because it lacks the specific context of the meeting. "Prompt engineering and contextual anchoring" involve providing the AI with clear instructions (e.g., "focus on control exceptions") and anchoring the request in specific data (e.g., "refer only to the attached meeting transcript"). According to ISACA, this technique directs the model's attention and reduces the likelihood of hallucinations or omissions.
Increasing "temperature" (Option A) would actually make the model more random and less reliable. Anchoring ensures the AI stays grounded in the actual facts of the audit walkthrough.
NEW QUESTION # 71
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?
Answer: B
Explanation:
Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIA™ Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.
"NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness." While RPA (A) automates repetitive tasks, it doesn't interpret content. Predictive analytics (C) forecasts trends, and autoregressive models (B) generate sequences, not summarize documents.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "NLP Applications in Audit Planning and Risk Identification"
NEW QUESTION # 72
Which of the following is the GREATEST challenge facing IS auditors evaluating the explainability of generative AI models?
Answer: B
Explanation:
The greatest challenge for IS auditors in evaluating the explainability of generative AI models is the changing nature of algorithms as AI continues to learn (option D). Generative AI models, especially those using advanced techniques like deep learning and reinforcement learning, often employ continuous or dynamic learning, which results in models that evolve over time. This adaptability can significantly hinder explainability because the logic, parameters, or decision pathways may shift with ongoing retraining or real- time learning.
The ISACA Advanced in AI Audit™ (AAIA™) Study Guide stresses that: "Continually learning AI systems present unique audit challenges, as their internal representations and reasoning can change after deployment, making it difficult to fully capture and explain the rationale for outputs at any given point." Other options, such as bias in input data or computational performance, are significant but do not pose as fundamental a challenge to explainability as a model whose internal workings can dynamically change.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Explainability and Dynamic Models"
NEW QUESTION # 73
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?
Answer: C
Explanation:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the " AI Policies " themselves are the most comprehensive support for governance objectives.
Policies provide the " enforceable standards " for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the " Source of Truth " that guides committees and stakeholders in their decision-making processes.
NEW QUESTION # 74
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