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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 2	Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
Topic 3	AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.

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ISACA Advanced in AI Audit Sample Questions (Q181-Q186):

NEW QUESTION # 181
Which of the following is the BEST way to ensure an AI model's outputs are effectively controlled for bias?

A. Technical details of model development processes are transparent.
B. The organization's AI policies include a clear definition of fairness.
C. Model training is restricted to data containing real-world human decisions.
D. Accuracy ranges for various demographic groups are similar.

Answer: D

Explanation:
The most objective evidence of bias control is "Performance Parity". If the accuracy, precision, and recall are "Similar across various demographic groups," it demonstrates that the model is functioning equitably for all users.

NEW QUESTION # 182
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?

A. Whether the validation data set utilized the same number of records as the training data sets
B. Whether the training, validation, and testing data sets were created in the correct order
C. Whether data leakage occurred from utilizing overlapping records in the data sets
D. Whether a sufficient number of records were utilized in the training data set

Answer: C

NEW QUESTION # 183
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. Increase class weights on recently labeled data only.
B. Train a larger model to absorb guideline variance.
C. Augment the model with additional layers to capture old and new labeling policies.
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 # 184
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations. Which of the following MOST effectively detects data drift related to the model's predictions?

A. Using adversarial testing to simulate scenarios that stress test the model's predictions
B. Conducting periodic model retraining to ensure alignment with updated patient data
C. Comparing incoming patient data distributions with the training data set
D. Applying overrides to allow healthcare professionals to correct the AI model's recommendations

Answer: C

NEW QUESTION # 185
An AI audit tool incorrectly flagged that business decisions were biased, leading to inappropriate management action plans. Which of the following can BEST prevent this risk?

A. Document assumptions and record scope.
B. Use different fairness definitions.
C. Reweight groups to oversample.
D. Apply explainable AI validation methods.

Answer: D

Explanation:
AI tools can produce " False Positives " in bias detection if they misinterpret the data. To prevent management from acting on incorrect audit findings, " Explainable AI (XAI) validation methods " (like SHAP or LIME) should be applied. XAI allows the auditor to see why the tool flagged a specific decision as biased.
If the tool ' s reasoning is flawed (e.g., it ignored a valid business justification), the auditor can correct the finding before it reaches management. This adds a necessary layer of " Auditor skepticism " and human validation to AI-driven audit insights.

NEW QUESTION # 186
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