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NEW QUESTION # 74
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?
Answer: A
Explanation:
An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIATM Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.
"A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence." While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.
NEW QUESTION # 75
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?
Answer: A
Explanation:
Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIATM Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.
"Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black- box models."
NEW QUESTION # 76
Which of the following is the PRIMARY benefit of using linear regression in machine learning (ML) when transparency and auditability are required?
Answer: B
Explanation:
Linear regression is a "transparent" or "white-box" model. Its primary benefit in an audit context is its interpretability; each coefficient directly indicates the influence of an input variable on the output. This allows auditors to easily verify the logic and ensure that no inappropriate or discriminatory variables are driving the results. While linear models are less prone to complex overfitting than deep learning, they are not immune to it. Their simplicity often results in lower predictive accuracy for complex data, but for auditability and compliance, the ability to explain the
"why" behind a prediction is paramount.
NEW QUESTION # 77
An IS auditor is evaluating a cybersecurity system that uses " agentic AI " for autonomous response. Which of the following is MOST important to consider?
Answer: B
Explanation:
" Agentic AI " possesses the autonomy to perform actions within a system. In a cybersecurity context, an agent might autonomously block an IP or shut down a database if it detects a threat. The most significant risk is that a " False Positive " could trigger an automated response that causes a massive service disruption.
Auditors must validate the existence of " Guardrails, " " Human-in-the-loop " approval for high-impact actions, and " Kill Switches " to halt the agent if it behaves erratically. While reduced intervention (Option B) is a benefit, it is also the primary driver of this operational risk.
NEW QUESTION # 78
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?
Answer: B
Explanation:
The AAIATM Study Guide stresses that inputting confidential or proprietary data into third-party generative AI tools may result in unauthorized data disclosure. These tools may store, process, or retrain on the input data, leading to privacy and intellectual property risks.
"When employees input sensitive data into external AI tools, organizations risk losing control over that information. This may result in regulatory non-compliance, legal exposure, and irreversible data leakage."
NEW QUESTION # 79
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