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質問 # 223
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?
正解：C
解説：
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.
質問 # 224
Which of the following is the MOST frequent cause of generative AI model hallucinations?
正解：D
解説：
Hallucinations-instances where a generative model produces factual errors or nonsensical information with high confidence-are primarily caused by " Inadequate data quality " in the training set. If the model is trained on data that is contradictory, incomplete, or contains " noise " (incorrect facts), it fails to learn accurate semantic relationships. The ISACA AAIA™ manual highlights that " Data Cleaning " and " Provenance " are essential to mitigate this. While weak controls (Option C) or poor change management (Option D) can lead to other risks, the mathematical root of the hallucination itself is typically found in the underlying quality and accuracy of the training data.
質問 # 225
Which regulatory framework introduces a risk-tiered classification (unacceptable, high, limited, minimal) for AI systems?
正解：D
解説：
The EU AI Act classifies AI systems into risk tiers, with high-risk systems subject to conformity assessments, documentation, and human oversight requirements.
質問 # 226
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?
正解：A
解説：
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIA™ Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors-such as competitors or bots- from submitting biased or harmful data. This control protects model training and outputs from being manipulated." Options A and C address technical data handling but not source authenticity. Option B may inadvertently incentivize biased reviews. Option D provides a robust control against poisoning.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Input Data Security and Adversarial Risk Mitigation"
質問 # 227
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
正解：C
解説：
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIATM Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.
質問 # 228
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