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ISACA AAIA Exam Syllabus Topics:

Section	Objectives
AI Governance and Risk
AI Auditing Tools and Techniques
AI Operations

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ISACA Advanced in AI Audit Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

A. Latency testing
B. Confusion matrix
C. Group analysis
D. Adversarial testing

Answer: B

Explanation:
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing. According to the AAIA™ Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made-false positives and false negatives-thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed-not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Evaluation Metrics"

NEW QUESTION # 92
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?

A. Perform testing of diverse scenarios to confirm outputs are within the acceptable range.
B. Increase the volume of training data to ensure the data set is fair and impartial.
C. Conduct threat analysis to identify unknown exposures.
D. Implement continuous monitoring of AI servers to detect anomalies in technical performance.

Answer: A

Explanation:
The risk described is thatcustomer-facing suggestionsmay be inappropriate, insensitive, or offensive. The BEST mitigation is toperform testing of diverse scenarios(B)-including edge cases, demographic variations, and sensitive contexts-to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlightsscenario-based testingandfairness/impact assessmentsas key practices, especially where recommendations directly influence customer interactions.
Increasing data volume (A) does not ensure fairness or sensitivity. Monitoring servers (C) focuses on technical health, not content appropriateness. Threat analysis (D) is important for security but does not directly address emotional or ethical impacts of model outputs. Therefore, structured,diverse scenario testing is the most targeted and effective approach.
References:
ISACA,AAIA Exam Content Outline- Domain 2 & Domain 5: Testing techniques; ethical and user-impact considerations.
ISACA AI guidance on scenario testing for fairness, appropriateness, and user impact.

NEW QUESTION # 93
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

A. Embedding AI audit tools in transactional systems without user training
B. Applying automated anomaly detection without human oversight
C. Utilizing unstructured data sources without standardized preprocessing
D. Training models on historical audit results generated prior to AI adoption

Answer: C

Explanation:
Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.
AAIA warns that improperly processed unstructured data leads to:
Incorrect text extraction
Lost contextual meaning
Feature extraction errors
Misclassification
Inaccurate audit evidence

NEW QUESTION # 94
Which metric is MOST important to consider when reviewing the performance of a machine learning model in avoiding false positive results?

A. Precision
B. Accuracy
C. Recall
D. F1 score

Answer: A

Explanation:
Precision measures the proportion of positive predictions that were actually correct. When avoiding false positives is the priority (e.g., fraud accusations, security alerts, loan denials), precision is the critical metric because:
High precision = very few false positives
Low precision = many false positives
AAIA stresses using appropriate performance metrics depending on risk context.

NEW QUESTION # 95
Which of the following should be applied to an AI system but are not typically used in traditional systems?

A. Controls to prevent data exfiltration
B. Controls to protect data privacy
C. Controls to manage data governance
D. Controls to monitor data poisoning

Answer: D

Explanation:
AI systems faceunique threatsnot commonly found in traditional IT environments, particularlydata poisoning, where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning-such as input provenance checks, anomaly detection on training data, and integrity validation pipelines-are emphasized in AAIA's coverage ofAI-specific vulnerabilities.
While privacy (A), data exfiltration (C), and data governance (D) controls are essential for all digital systems, monitoring for data poisoningis uniquely critical for AI because poisoned inputs can lead to faulty predictions, safety issues, or systemic bias. AAIA specifically highlights data poisoning as a distinct threat requiring specialized controls.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI.
ISACA AI security guidance discussing poisoning and integrity attacks.

NEW QUESTION # 96
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