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NEW QUESTION # 169
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: B
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.
Option A is less critical because increased complexity is not the core risk. Option B is important but secondary; documentation issues do not inherently degrade model reliability. Option D is an efficiency issue, not a risk to output integrity.
Therefore, compromised reliability due to poor-quality training data is the most significant risk.
References:
AAIA Domain 2: Data Management Specific to AI (data validation, verification, data quality).
AAIA Domain 1: Governance and Risk Controls for AI.
NEW QUESTION # 170
Which of the following considerations should be prioritized when using an AI tool to select a sample for conducting an audit of a financial institution's transaction processing system?
Answer: C
Explanation:
In an audit context,transparencyof sampling is essential for demonstrating that the sample is fair, unbiased, and aligned with the audit objectives. When an AI tool selects samples for testing financial transactions, auditors must be able to explain and defendhowthe sample was generated-particularly to management, regulators, and external stakeholders. Option A directly supports AAIA's focus onaudit planning, sampling methodologies, and AI audit evidence.
High throughput (option B) and speed (option C) are beneficial but secondary to methodological soundness and explainability. Option D (historical performance) can be helpful but does not guarantee current transparency or appropriateness in new contexts. For AI-enabled sampling, the priority is that theselection logic is understandable, documented, and reproducible, ensuring audit defensibility.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (Audit Testing and Sampling Methodologies; Audit Evidence Collection Techniques).
ISACA auditing guidance on sampling and transparency in AI-assisted audit procedures.
NEW QUESTION # 171
An auditor discovers that a model's training data was collected five years ago and the business environment has changed significantly. What risk does this MOST directly represent?
Answer: B
Explanation:
Concept drift occurs when the statistical relationship between inputs and the target variable changes over time, often due to shifting real-world conditions -- distinct from data drift, which concerns the input distribution alone.
NEW QUESTION # 172
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:
Answer: C
Explanation:
Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIA™ Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.
"To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods." While metadata (A) and developer interviews (C) can supplement findings, only B provides objective, reproducible evidence. Option D may reflect real-world interactions but lacks the control and consistency required in an audit.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Fairness and Bias Testing in AI Systems"
NEW QUESTION # 173
An AI team is developing a natural language processing (NLP) chatbot to assist customers. The collected data includes informal speech and multiple languages. Which pre-processing step is MOST important?
Answer: C
Explanation:
In NLP, the quality of training depends on the " Standardization " of input data. Informal language (slang, typos) and mixed languages create high " noise, " making it difficult for the model to learn semantic meaning.
Using dictionaries to normalize text and translation services to create a coherent dataset is the most foundational step. While tokenization (Option C) and entity recognition (Option B) are standard parts of the NLP pipeline, they cannot function effectively if the underlying text is inconsistent or fragmented. Removing non-English data (Option D) would result in a non-representative model that fails to meet the needs of a diverse customer base.
NEW QUESTION # 174
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