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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting |
| AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation |
| AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability |
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NEW QUESTION # 76
Which of the following is the PRIMARY reason to lower AI model temperature?
Answer: A
Explanation:
Temperature is a hyperparameter in language model generation that controls output randomness. Lower temperatures make the model more deterministic-concentrating probability mass on the most likely tokens and producing more consistent, predictable outputs. Higher temperatures introduce more randomness and diversity.
Why B is Correct: According to ISACA AAIR model configuration guidance, lowering model temperature is primarily used to enhance consistency and accuracy of outputs. In production applications requiring reliable, reproducible responses-such as customer service, compliance reporting, or technical documentation-lower temperature ensures the model consistently generates the most appropriate response based on its learned knowledge, reducing variability and improving output quality.
Why A is Wrong: Temperature adjustment does not directly mitigate bias. Bias in AI models is a function of training data and model architecture, not output randomness. A biased model at low temperature will consistently generate biased outputs; lowering temperature may actually make bias more persistent by reducing variation.
Why C is Wrong: Diversifying ideas and recommendations is achieved by increasing temperature, not lowering it. Higher temperature is used for creative tasks where variety is valuable; lower temperature is used for tasks requiring precision and consistency.
Why D is Wrong: Model temperature has no direct relationship to computational energy consumption. Energy use is primarily driven by model size, computation requirements, and inference frequency-not the temperature parameter.
NEW QUESTION # 77
Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?
Answer: C
Explanation:
Data asset management for large-scale AI programs involves processing, cataloging, and maintaining vast quantities of structured and unstructured data. AI-powered automation addresses the scalability challenges of manual data management processes.
Why D is Correct: The ISACA AAIR AI capabilities guidance identifies automating data cleaning and metadata tagging as the greatest practical benefit of AI-powered data asset management. Large datasets- often containing millions of records-require consistent preprocessing and cataloging to be usable for AI training and governance. AI automation achieves this at scale, with speed and consistency that manual processes cannot match, improving data quality and discoverability across the organization.
Why A is Wrong: Justifying synthetic data usage is a model development strategy decision, not a data asset management benefit. The justification for synthetic data depends on use case requirements, not AI automation capability.
Why B is Wrong: AI tools can support security monitoring but do not inherently reduce the initial impact of data poisoning or exfiltration attacks. Security outcomes depend on specific defensive AI applications, not general data management automation.
Why C is Wrong: Overfitting identification during model training is a model development monitoring activity. While AI can support training analytics, this is a narrow benefit compared to the broad, scalable data asset management value of automated cleaning and tagging.
NEW QUESTION # 78
A risk practitioner is concerned that an AI model's responses have become more inaccurate over time, leading to diminished customer trust. Which of the following should the risk practitioner recommend be done FIRST?
Answer: B
Explanation:
Incident response for AI model degradation should follow a structured diagnostic process. Before implementing any corrective action, the scope and nature of the accuracy issues must be understood to ensure the response is appropriate and targeted.
Why D is Correct: According to ISACA AAIR incident response guidance, the first step when AI model accuracy deteriorates is to assess the impact-understanding which specific features are affected, how model outputs have changed, and what the business consequences are. This diagnostic step informs all subsequent decisions about whether to retrain, add validation cycles, or take the system offline. Acting without this assessment may waste resources on inappropriate responses or leave critical issues unaddressed.
Why A is Wrong: Adding validation review cycles is a process change that may be appropriate but cannot be determined without first understanding the nature and scope of the accuracy problem. Reviews address a symptom without diagnosing the cause.
Why B is Wrong: Taking the model offline and backing it up is a drastic operational measure that may be disproportionate to the actual issue. This decision requires understanding the severity and scope of the problem, which requires impact assessment first.
Why C is Wrong: Full model retraining is resource-intensive and may not address the root cause if the problem is not training data staleness. Impact assessment must precede the decision to retrain.
NEW QUESTION # 79
AI tools can BEST help to mitigate supply chain risk by:
Answer: D
Explanation:
Supply chain risk management requires anticipating disruptions before they materialize. AI's most powerful supply chain contribution is its ability to analyze vast datasets-including signals from suppliers, logistics networks, geopolitical indicators, and environmental data-to predict disruptions with accuracy and lead time that human analysts cannot achieve.
Why B is Correct: The ISACA AAIR AI capability guidance identifies predictive disruption identification as the most significant supply chain risk mitigation AI provides. By processing diverse data signals and identifying patterns that precede supply chain failures, AI enables proactive risk management-allowing organizations to pre-position inventory, identify alternative suppliers, or adjust production schedules before disruptions affect operations.
Why A is Wrong: Automating inventory management is an operational efficiency application. While valuable, it manages existing stock levels rather than predicting and preventing supply disruptions.
Automation cannot anticipate future risks not embedded in current inventory patterns.
Why C is Wrong: Historical security control gap identification is a security audit function. Identifying past security weaknesses does not directly mitigate supply chain disruption risks, which may arise from entirely different categories of risk.
Why D is Wrong: Sentiment analysis on supplier reputation provides one qualitative input to supplier risk assessment. While useful for monitoring reputational signals, it captures only a narrow dimension of supply chain risk compared to comprehensive predictive disruption modeling.
NEW QUESTION # 80
Which of the following poses the GREATEST challenge related to the protection of intellectual property generated by AI solutions?
Answer: D
Explanation:
Traditional intellectual property law was designed for human-created works. AI-generated content sits in a legal grey zone because current copyright frameworks in most jurisdictions do not clearly establish who-if anyone-holds copyright in outputs created autonomously by AI systems.
Why C is Correct: According to ISACA AAIR, the lack of regulatory clarity around AI-generated content copyright is the greatest IP challenge because it creates fundamental uncertainty about ownership, transferability, and enforceability of rights in AI outputs. Without clear legal status, organizations cannot confidently assert ownership, license AI-generated materials, or prevent competitors from copying outputs.
This uncertainty pervades commercial agreements, licensing strategies, and competitive protection.
Why A is Wrong: Zero-data retention policies actually protect intellectual property by ensuring vendor systems do not retain proprietary input data. This represents a protective measure, not a challenge.
Why B is Wrong: Training material customization for confidential data handling is a workforce education challenge. While important for data protection, it does not represent the primary IP challenge from AI- generated content.
Why D is Wrong: Low-risk use cases like administrative tasks present minimal IP concerns because the outputs are typically not commercially significant or protectable. The IP challenge is greatest for creative, analytical, and proprietary outputs.
NEW QUESTION # 81
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