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NEW QUESTION # 250
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?
Answer: D
NEW QUESTION # 251
An IS auditor examining change management procedures for an AI system observes inconsistent training data validation and verification protocols prior to model retraining. Which of the following is the MOST significant risk in this context?
Answer: D
Explanation:
When training data validation is inconsistent, the most severe risk is that the AI model may learn from incorrect, incomplete, biased, or corrupted data. This directly leads to a degradation of system reliability (option C), which manifests as inaccurate predictions, higher error rates, bias, or unstable behavior.
AAIA emphasizes that data validation prior to retraining is one of the most important controls because model behavior is fully dependent on training data integrity. If the quality and correctness of the data cannot be guaranteed, the resulting model outputs become unreliable, which can undermine compliance, operational decisions, and user trust.
NEW QUESTION # 252
Which of the following AI solutions is BEST used to generate standardized reports using unstructured customer feedback?
Answer: C
Explanation:
Natural Language Processing (NLP) is the branch of AI specifically designed to handle unstructured text. In an audit or business context, NLP tools can perform " sentiment analysis " and " entity extraction " on large volumes of customer feedback to identify common themes, complaints, or positive trends. According to ISACA, NLP is the optimal choice for converting qualitative, human-written text into structured, standardized reports that auditors can use for objective analysis. Anomaly detection (Option A) would only find unusual feedback, and predictive analytics (Option C) would attempt to forecast future feedback, but NLP is the foundational engine required to understand and standardize the existing content.
NEW QUESTION # 253
An organization is conducting an audit of an AI decision-making system being used for talent recruitment. Which of the following is MOST critical to evaluate in order to ensure the system meets stakeholder needs?
Answer: B
Explanation:
While predictive accuracy and timeliness are operational requirements, "Decision fairness" is the most critical ethical and legal consideration in recruitment AI. Stakeholders, including candidates, regulators, and management, require assurance that the system does not discriminate against protected groups based on gender, age, or ethnicity. The ISACA AAIATM Study Guide emphasizes that for high-impact human resource applications, auditors must prioritize fairness testing and bias assessments to prevent reputational damage and legal liability. A system that is highly accurate but biased fails to meet the ethical standards and "stakeholder needs" of modern governance.
NEW QUESTION # 254
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?
Answer: C
Explanation:
In neural network-based credit scoring, the model's complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak,
"biased inputs" or "noise" from various sources can seep into the training and inference pipelines.
According to the ISACA AAIATM framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions.
NEW QUESTION # 255
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