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NEW QUESTION # 212
The internal audit department of a large law firm plans to implement an AI solution to review and analyze contracts stored in its enterprise resource planning (ERP) system. Which of the following AI technologies is MOST suitable for this requirement?
Answer: C
Explanation:
Natural Language Processing (NLP) is the specific branch of AI focused on the interaction between computers and human language. For tasks involving the review, summarization, and extraction of insights from unstructured text documents like legal contracts, NLP is the most suitable technology. It allows the system to understand semantic meaning, identify clauses, and detect inconsistencies within a legal context.
NEW QUESTION # 213
Which of the following is the BEST reason that recurrent neural networks enable language translation of documents?
Answer: B
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 thesequential processingcapability.
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 # 214
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
Answer: B
Explanation:
According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle." While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Governance Frameworks and Compliance"
NEW QUESTION # 215
Which of the following is the GREATEST risk when a generative AI tool used for threat detection produces inaccurate or misleading information?
Answer: B
Explanation:
In the context of cybersecurity and threat detection, the "accuracy" of AI outputs is a matter of organizational safety. The greatest risk of inaccurate information (such as false negatives) is that
"Potential threats may be overlooked," leading to undetected breaches or system compromises.
This occurs when the model fails to flag a genuine anomaly as suspicious.
NEW QUESTION # 216
An IS auditor is reviewing change management documentation of an AI model. Which of the following would pose the GREATEST risk to the model?
Answer: B
Explanation:
In AI development, a " seed " ensures that random processes (like weight initialization) are reproducible. If an A/B test compares two models using different seeds, the auditor cannot tell if the performance difference is due to the model changes or simply due to " random luck " in how the weights were initialized. This invalidates the test results. For a fair " apple-to-apples " comparison, the seed should remain consistent.
Tuning on a training set (Option B) is standard, though it risks overfitting; however, the lack of scientific control in testing (Option C) is a more immediate risk to the integrity of the change management process.
NEW QUESTION # 217
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