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NEW QUESTION # 309
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 # 310
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
Answer: D
Explanation:
Dynamic pricing algorithms can unintentionally discriminate against protected groups if trained on biased data or poorly designed features. The AAIA highlights fairness testing as a mandatory requirement in any AI solution that impacts customers financially or socially.
Specific ethical tests are needed to ensure:
Pricing does not vary unfairly based on demographics
Sensitive attributes ( ethnicity, age, gender ) are not inferred or misused Customer segmentation does not disproportionately disadvantage protected groups Historical biases do not propagate into automated pricing
NEW QUESTION # 311
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?
Answer: B
Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"
NEW QUESTION # 312
An insurance company uses an AI model to set premium rates. To align with AI-related policies on fairness, which of the following is the FIRST course of action?
Answer: A
Explanation:
AAIA guidance states thatfairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, thefirstcourse of action is to review training data (option B) to identify:
* Skewed demographic distributions
* Missing or underrepresented populations
* Inappropriate use of sensitive attributes
* Incorrect labels or improperly encoded variables
* Historical decisions that may propagate discrimination
Only after identifying the existence and nature of bias can an organization move on to remediation steps such as retraining (C) or comparing alternate model behavior (A).
Allowing customers to contest premiums (D) is a post-decision remedy, not a fairness evaluation step.
AAIA emphasizes addressing bias at theroot-the training data-before adjusting the model or outputs.
References:
AAIA Domain 5: Fairness, Bias Identification, Ethical AI Principles
AAIA Domain 2: Data Governance and Input Evaluations
NEW QUESTION # 313
An IS auditor is planning an audit covering a vendor-provided and supported AI model used by the organization to predict train track maintenance. Which of the following is the BEST approach for the IS auditor to test the model?
Answer: C
Explanation:
When auditing vendor-provided (third-party) AI models, the organization often lacks access to the underlying source code, neural network architecture, or weights. In such cases, the AAIA™ manual recommends " Black Box Testing " as the most practical and effective approach. This involves testing the model based solely on its inputs and outputs to verify its predictive accuracy, fairness, and reliability without needing internal technical details. White box testing (Option B) and code reviews (Option D) are typically unfeasible due to proprietary restrictions. Black box testing allows the auditor to validate that the model meets business requirements and operates within acceptable risk tolerances as defined in the vendor agreement.
NEW QUESTION # 314
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