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| Section | Objectives |
|---|---|
| AI Auditing Tools and Techniques | - AI Audit Planning
|
| AI Operations | - AI System Lifecycle
|
| AI Governance and Risk | - AI Security and Controls
|
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NEW QUESTION # 284
Which of the following should be an IS auditor's GREATEST concern when reviewing an anomaly detection process implemented for a high-risk AI system?
Answer: D
Explanation:
In ahigh-risk AI system, anomaly detection often serves as a frontline control to flag irregularities in input data and model behavior. TheGREATEST concernfor an IS auditor is when the processfails to identify anomalies that can bias training data(A), because undetected anomalies can fundamentally distort model learning and outputs. This can lead to systemic bias, incorrect decisions, safety risks, and regulatory breaches.
Option B (lack of regular quality reviews) is serious but is partially addressed if anomaly detection is effective. Option C (infrequent updates) may degrade detection performance over time but is less critical than outright failure to detect harmful anomalies. Option D (staff training) is important for operational effectiveness but still secondary to the technical failure to catch bias-inducing anomalies. AAIA stresses that data integrity and monitoring controlsare paramount in high-risk contexts.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Supervision of AI Solutions, data monitoring, and anomaly detection).
ISACA AI risk materials focusing on high-risk AI oversight and data integrity.
NEW QUESTION # 285
A healthcare AI tool recommends treatments with high success rates but significant risk. The hospital prioritizes patient safety over innovation. What is the BEST course of action?
Answer: C
Explanation:
AI systems must align with theorganization's risk appetite and ethical principles, especially in healthcare where patient safety is paramount. The BEST action is toadjust the AI's parameters(A) so the recommendations reflect the hospital's conservative risk tolerance, reducing the frequency of high-risk suggestions. AAIA stresses AI governance alignment with organizational risk appetite, treatment guidelines, and ethical priorities.
Option B is overly disruptive and eliminates beneficial AI capabilities. Option C is necessary for privacy but does not address treatment safety. Option D limits utility but doesn't correct underlying alignment issues. The core issue ismodel alignment with ethical and safety standards, making option A the correct choice.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical Principles in AI (alignment with risk tolerance, safety, beneficence).
NEW QUESTION # 286
An AI model predicts vehicle component failures using data collected at different frequencies and formats based on car type. Which of the following is the BEST course of action when evaluating data input requirements for the model?
Answer: B
Explanation:
For reliable model performance and meaningful comparisons across inputs,data consistencyis essential.
Standardizing sensor data frequency and formats ensures that the model receives aligned time steps and coherent feature structures, reducing the risk of spurious patterns, missing signals, and biased predictions.
This is aligned with AAIA's focus ondata quality, data balancing, and data preparationin AI Operations.
Option B ignores frequency and formatting differences, likely introducing noise and misalignment. Option C may sometimes be valid, but it increases complexity, maintenance overhead, and may still require consistent preprocessing pipelines. Option D addresses only one data source and does not solve the problem of heterogeneous sensor data. The most robust operational approach is to define clear data input requirements and standardize the sensor data(option A) before training.
References:
ISACA,AAIA Exam Content Outline- Domain 2: AI Operations (Data Management Specific to AI - data quality, data balancing, data security).
ISACA AI operations guidance on data pipelines and preprocessing for AI models.
NEW QUESTION # 287
An IS auditor reviewing documentation for an AI model notes that the modeler utilized a K-means clustering algorithm, which clusters data into categories for correlations and analysis. Which of the following is the MOST important risk for the auditor to consider?
Answer: C
NEW QUESTION # 288
An IS auditor is assessing the implementation of AI tools for evidence collection involving multiple data sources. Which of the following outcomes BEST indicates that AI-driven evidence collection has improved the audit process?
Answer: C
Explanation:
AI-driven evidence collection should enhanceefficiency and accuracy. The BEST indicator of improvement isreduced time spent gathering data with fewer errors(B), showing that AI has streamlined data extraction, consolidation, and initial validation without compromising quality. AAIA's content on AI in audit processes highlights benefits such asautomation of repetitive tasks, improved coverage, and higher-quality evidence.
Extended timelines for retraining (A) suggest inefficiency rather than improvement. Eliminating human judgment (C) is neither realistic nor recommended; professional skepticism and auditor judgment remain essential. Relying on unstructured data with minimal cleansing (D) can increase risk of misinterpretation or noise. Therefore, more efficient and accurateevidence compilationis the clearest positive outcome.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (efficiency and quality improvements in audit).
ISACA internal audit and data analytics guidance on AI's value in evidence collection.
NEW QUESTION # 289
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