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| Section | Weight | Objectives |
|---|---|---|
| AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - Privacy and Data Governance Programs - AI Risk Management - Ethics, Regulations, and Standards for AI - AI Governance and Program Management |
| AI Auditing Tools and Techniques | 21% | - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques - Audit Testing and Sampling Methodologies - Data Quality and Analytics for AI Audit - Audit Planning and Design |
| AI Operations | 46% | - Incident Management and Resilience - Performance Monitoring and Evaluation - Operational Controls and Readiness - AI System Lifecycle and Deployment - Third-Party and Supply Chain Risk |
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NEW QUESTION # 216
In deep learning neural networks, which of the following layers is considered MOST important for an IS auditor to evaluate because it performs feature extraction to mimic human decision-making?
Answer: C
Explanation:
In the context of Neural Networks, the " Hidden Layers " are where the actual transformation and feature extraction occur. These layers sit between the input and output, processing data through weighted connections to identify complex patterns. For an IS auditor, evaluating the hidden layers is critical because they represent the " logic " of the model that emulates human-like cognition. While input and output layers are transparent, the hidden layers often lack interpretability, leading to risks of hidden bias or non-deterministic behavior.
Understanding the depth and activation functions of these layers helps auditors assess the model ' s complexity and its susceptibility to errors.
NEW QUESTION # 217
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
Answer: A
Explanation:
Representativeness means that training data accurately reflects the current real-world environment in which the AI system operates. The BEST way to ensure this is by verifying that the training data remains relevant and aligned with evolving real-world conditions (C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
NEW QUESTION # 218
In the context of an AI implementation, which of the following actions is MOST critical for an organization's change management program?
Answer: D
Explanation:
The AAIATM Study Guide emphasizes that AI implementations introduce dynamic and non- deterministic elements into systems, increasing the risk associated with changes. A comprehensive, AI-specific risk assessment is therefore the most critical component of a change management program to ensure that updates, retraining, or parameter adjustments do not introduce vulnerabilities or unintended consequences.
"Risk assessments tailored to AI are crucial because changes to models, training data, or infrastructure can affect performance, ethical compliance, or expose the system to new threats. A standard IT change review is often insufficient."
NEW QUESTION # 219
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 AuditTM (AAIATM) 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."
NEW QUESTION # 220
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: C
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 # 221
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