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NEW QUESTION # 319
During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization's risk tolerance. Which of the following is the MOST appropriate response?
Answer: C
NEW QUESTION # 320
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: D
Explanation:
In predictive maintenance use cases--such as detecting turbine failure--the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIATM Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
NEW QUESTION # 321
An organization has exhausted its internal data sources to train an AI model. Which of the following is the BEST source to obtain new data?
Answer: B
Explanation:
When seeking external data for AI training, organizations must navigate significant legal and ethical risks regarding Intellectual Property (IP). " Copyright-free data " (or data used under clear licensing) is the safest and most ethical source. Using " web-scraped " data (Option A) without permission often leads to copyright infringement lawsuits and regulatory violations (e.g., using personal data without consent). " Shadow data " (unmanaged internal data) poses security risks. For long-term sustainability and audit compliance, using properly licensed or public-domain data ensures the model ' s outputs do not violate the IP rights of third parties.
NEW QUESTION # 322
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?
Answer: B
Explanation:
To assess compliance with intellectual property (IP) and data rights, the IS auditor must review documented data usage agreements that specify ownership, licensing, consent, and limitations of use. The AAIATM Study Guide underscores the importance of verifying that the data used to train or feed AI models is obtained and utilized within legal and contractual boundaries.
"Auditors must review data usage agreements to validate whether the organization has appropriate rights to use, distribute, or transform data inputs, especially where third-party or sensitive data is involved."
NEW QUESTION # 323
The BEST way to prevent sensitive information disclosure by large language model (LLM) chatbots is through:
Answer: C
Explanation:
Large Language Models (LLMs) have the capacity to memorize and unintentionally reveal sensitive information if not properly managed. As per the AAIA™ Study Guide, data masking is a critical technique that prevents the exposure of personally identifiable information (PII) or confidential content by obscuring or replacing sensitive parts of the data during training or interaction.
"Data masking ensures that training data used for LLMs does not contain real sensitive identifiers. Unlike sanitization, masking modifies data to maintain utility while eliminating exposure risk." Manual monitoring and access controls are supportive security measures, and data sanitization helps remove content but may not preserve the data's structure. Data masking offers the most proactive and technically robust solution.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Privacy and Information Protection in AI Systems"
NEW QUESTION # 324
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