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ISACA Advanced in AI Audit Sample Questions (Q53-Q58):
NEW QUESTION # 53
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?
- A. Precision
- B. Accuracy
- C. Specificity
- D. Recall
Answer: D
NEW QUESTION # 54
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
- A. Separating multiple data attributes within one field into individual attribute columns
- B. Filling blank attributes in records with the mean, median, or mode within a grouping
- C. Identifying and deleting duplicate entries in the data set
- D. Placing numerical data into bins or buckets for a manageable quantity of correlations and result analyses
Answer: B
NEW QUESTION # 55
When an IS auditor is reviewing results from an AI system, which of the following would cause the GREATEST risk?
- A. Cascading failures of AI system outputs
- B. Inability to identify where an AI system is housed
- C. System output not being checked for inconsistencies
- D. Difficulty of documenting AI algorithm processes
Answer: C
NEW QUESTION # 56
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?
- A. Maximize browser extension functionality.
- B. Remove user access to browser extensions.
- C. Use open-source browser extensions.
- D. Minimize browser extension functionality.
Answer: D
Explanation:
Excessive agencyoccurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is tominimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance onlimiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open- source extensions (D) improve transparency but do not inherently control excessive agency.Scope limitation is the most targeted and effective mitigation.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Governance, AI Autonomy, and Risk Controls.
NEW QUESTION # 57
An AI developer notices that the labeling guidelines changed during the project and asks an IS auditor for advice. Which of the following is the auditor's BEST recommendation to perform prior to retraining?
- A. Augment the model with additional layers to capture old and new labeling policies.
- B. Train a larger model to absorb guideline variance.
- C. Increase class weights on recently labeled data only.
- D. Relabel stratified samples to estimate shift and adjust data sets accordingly.
Answer: D
Explanation:
Whenlabeling guidelines change, there is a risk oflabel shift-inconsistency between older and newer labels. The BEST recommendation is torelabel stratified samplesacross the dataset to estimate the extent of the shift and adjust datasets accordingly (D). This allows the team to quantify inconsistencies, align labels to the updated guidelines, and ensure that retraining reflects a coherent labeling policy. AAIA emphasizes managingdata and label qualityas core aspects of AI operations.
Adding layers (A) or training a larger model (C) does not solve inconsistent labeling; it may even entrench confusion. Increasing class weights on recent data only (B) can bias the model and does not repair inconsistent historical labels. Therefore,relabeling structured, representative samples to assess and correct label shiftis the most appropriate and audit-aligned recommendation.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (labeling consistency, data drift, label shift).
ISACA AI risk guidance on dataset versioning and labeling policy changes.
NEW QUESTION # 58
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