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NEW QUESTION # 263
Which of the following is the BEST reason that recurrent neural networks enable language translation of documents?
Answer: C
Explanation:
Recurrent neural networks (RNNs) and their variants (such as LSTMs and GRUs) are designed to handle sequential data , capturing dependencies across time or position in a sequence. In language translation, words and phrases must be interpreted in context, where the meaning of a word depends on preceding (and, in advanced architectures, following) tokens. RNNs maintain internal state across steps, allowing the model to encode information from earlier parts of the sentence when predicting later outputs.
Option B (association rules) refers more to classical data # mining methods, not the core reason RNNs work for translation. Option C (grid data) is more relevant to convolutional neural networks used for images.
Option D (unidirectional) is not inherently an advantage; in fact, bidirectional models are often preferred.
Therefore, the key property enabling RNN use in translation is the sequential processing capability.
References:
ISACA, AAIA Exam Content Outline - Domain 1: AI Models, Considerations, and Requirements (types of AI, machine learning models).
ISACA, general AI fundamentals content used in AAIA preparation (sequence models for NLP).
NEW QUESTION # 264
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?
Answer: B
Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIA™ Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies." While hallucinations (C) and privacy notices (B) are important concerns, the primary auditor responsibility is to ensure that source data is accurate and suitable. Therefore, D is the correct response.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Auditor Responsibility and AI Input Validation"
NEW QUESTION # 265
Which of the following would be the BEST time to create a data governance plan for a new AI solution?
Answer: C
Explanation:
In AI development, " Privacy by Design " and " Governance by Design " are fundamental principles. The data governance plan should be created " As part of the initial design " phase. This ensures that the requirements for data quality, lineage, labeling, and privacy are integrated into the architecture from the beginning.
According to the AAIA™ framework, attempting to retrofit governance during the testing (Option A) or production (Option B) phases is often impossible or cost-prohibitive, as the data may have already been corrupted or collected in a non-compliant manner. Planning at the design stage mitigates the risk of building on a flawed foundation.
NEW QUESTION # 266
Which metric is MOST important to consider when reviewing the performance of a machine learning model in avoiding false positive results?
Answer: C
Explanation:
Precision measures the proportion of positive predictions that were actually correct. When avoiding false positives is the priority (e.g., fraud accusations, security alerts, loan denials), precision is the critical metric because:
High precision = very few false positives
Low precision = many false positives
AAIA stresses using appropriate performance metrics depending on risk context.
NEW QUESTION # 267
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: D
Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations-where generative models produce fabricated or misleading outputs. The AAIA™ Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI"
NEW QUESTION # 268
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