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ISACA Advanced in AI Audit Sample Questions (Q266-Q271):
NEW QUESTION # 266
An IS auditor is looking to expedite reporting for an audit with complex issues. Which of the following would be the MOST effective way for the auditor to use generative AI?
- A. Developing a draft of an executive summary based on detailed findings and audit scope
- B. Revising audit background and scope information based on new information from management
- C. Developing action items discussed in closing meetings for management action plans
- D. Revising audit conclusions with precise verbiage to describe the audit observations
Answer: A
NEW QUESTION # 267
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
- A. Training data is manually reviewed for bias.
- B. Training data remains relevant and reflects evolving real-world conditions.
- C. Synthetic data is used to train the AI systems.
- D. Data validation processes are automated and consistently performed.
Answer: B
Explanation:
Representativeness means that training data accurately reflects thecurrent real-world environmentin which the AI system operates. The BEST way to ensure this is by verifying that thetraining data remains relevant and aligned with evolving real-world conditions(C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
Manual review (A) is limited in scope and scale. Automated validation (B) helps detect errors but does not ensure data reflects the real world. Synthetic data (D) supplements but does not guarantee representativeness unless calibrated properly. Therefore,continuous relevance and contextual alignmentis the most important factor.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data relevance, drift detection, representativeness).
NEW QUESTION # 268
The PRIMARY purpose of maintaining an audit trail in AI systems is to:
- A. Analyze model accuracy and fairness.
- B. Facilitate transparency and traceability of decisions.
- C. Ensure compliance with regulatory standards for AI.
- D. Measure computational efficiency.
Answer: B
Explanation:
Audit trails in AI systems serve to document the inputs, processes, and outputs of AI decisions, allowing stakeholders and auditors to trace how decisions were made. The AAIA™ Study Guide identifies transparency and traceability as the core functions of an AI audit trail.
"Maintaining audit logs is critical to explainability and accountability. It ensures that decisions made by AI systems can be reconstructed and assessed for accuracy, ethics, and legality." Although compliance (D) is an outcome and fairness (B) a goal, only A captures the foundational purpose of audit trails in AI systems.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Auditability and Logging Mechanisms"
NEW QUESTION # 269
A retail organization uses an AI model to analyze customers' purchase history in order to offer personalized discounts. Which of the following practices represents the MOST ethical use of customer data?
- A. Providing the public with access to review and audit the data set of collected customer information
- B. Retaining and analyzing all available customer data to ensure unbiased recommendations
- C. Sharing customer purchase data with third-party vendors to improve advertising and communication
- D. Utilizing customer purchase data only after obtaining explicit consent and allowing customers to opt out
Answer: D
Explanation:
The ethical use of customer data is rooted in respecting privacy, maintaining informed consent, and enabling data subjects to exercise control over their personal information. The AAIA™ Study Guide clearly outlines that obtaining explicit consent and providing opt-out capabilities align with principles of data protection and ethical AI.
"Ethical AI implementation includes transparency in data collection, clear consent mechanisms, and the right of users to opt out or control their personal data usage. Retail and consumer applications must ensure that personalized services do not override these data subject rights." Options B and D violate principles of minimal data use and consent, while C may create unnecessary privacy exposure. Therefore, A is the correct and most ethical practice.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Informed Consent and Customer Privacy"
NEW QUESTION # 270
Which of the following BEST helps in detecting AI model drift?
- A. Evaluating model accuracy rates for stability and performing qualitative data analysis
- B. Using linear regression techniques and conducting cluster analysis
- C. Engaging periodic external reviews of model outputs and identifying root causes
- D. Establishing a model performance baseline and implementing continuous monitoring
Answer: D
Explanation:
Detecting model drift requires a point of comparison and real-time visibility. The AAIA™ manual identifies the best practice as " Establishing a performance baseline " (using metrics like accuracy or F1-score from the initial validation phase) and then " Implementing continuous monitoring " to track those metrics as the model processes live production data. Any significant deviation from the baseline serves as an early warning that the model ' s environment has shifted and retraining is necessary. Periodic reviews (Option A) are helpful but may not detect drift quickly enough to prevent erroneous decisions in dynamic environments.
NEW QUESTION # 271
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