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NEW QUESTION # 110
The accuracy of an AI model used for product recommendations, trained on data from a specific year, is declining two years later as customer buying patterns have shifted toward sustainable products. Which of the following is the MOST likely cause?
Answer: D
Explanation:
Data drift (also known as model drift or concept drift) refers to the phenomenon where the statistical properties of the target variable or the input data change over time, leading to a decline in model performance.
In this scenario, the fundamental shift in customer preferences toward sustainability represents a change in the real-world environment that the model ' s original training data no longer reflects. As stated in the AAIA™ guidelines, models operating in dynamic environments like retail require continuous monitoring to detect when historical patterns are no longer predictive. Covariate shift (Option D) is a sub-type of drift specifically involving changes in the distribution of input features, but " Data drift " is the broader, most appropriate term for the observed performance degradation.
NEW QUESTION # 111
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?
Answer: B
Explanation:
Class imbalance occurs when one or more classes are underrepresented in the training data. The GREATEST concern is model bias (C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate into systematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).
NEW QUESTION # 112
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
Answer: A
NEW QUESTION # 113
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?
Answer: A
Explanation:
When a dataset lacks sufficient examples of a " minority class " (e.g., rare diseases or specific demographic groups), the model will likely develop a bias toward the majority class. The BEST technical recommendation is to " Augment the data through synthesizing " (using techniques like SMOTE or GANs). Synthetic data generation creates artificial but statistically realistic minority samples, allowing the model to learn the characteristics of those groups without requiring more real-world data, which may be impossible to obtain.
Relabeling (Option D) does not solve the underlying scarcity of the data needed for diverse representation.
NEW QUESTION # 114
An IS auditor is evaluating an organization's incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?
Answer: A
Explanation:
AI-related incidents often differ significantly from traditional IT incidents due to their dependence on data, model behavior, and algorithm performance. According to the AAIATM Study Guide, incident management programs must include capabilities specifically tailored to AI, such as detecting and mitigating model drift and safeguarding against data poisoning or integrity attacks.
"AI incident response frameworks must account for issues unique to machine learning, including model drift, adversarial inputs, and data integrity breaches. An effective program incorporates detection, response, and recovery mechanisms for these AI-specific threats."
NEW QUESTION # 115
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