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>> AAIA Valid Exam Syllabus <<
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NEW QUESTION # 210
A health organization has deployed an AI model to analyze chest X-rays. The model reports high accuracy, but thresholds are unclear and performance is not broken down by patient demographics. Why is accuracy alone insufficient to evaluate this model?
Answer: D
Explanation:
The ISACA AAIA™ manual warns against " Aggregate Accuracy " in high-risk applications like healthcare.
A model might be 95% accurate overall but have a 40% error rate for a specific minority demographic (e.g., based on age or ethnicity). This creates " Hidden Fairness and Safety Risks " where patients from certain groups are consistently misdiagnosed. Auditors must recommend " Stratified Evaluation, " where performance metrics (Precision, Recall) are calculated separately for each subgroup to ensure equitable outcomes and patient safety.
NEW QUESTION # 211
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?
Answer: D
Explanation:
Differential privacy introduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data--highly sensitive and subject to strict privacy laws--this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.
NEW QUESTION # 212
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 # 213
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:
Answer: C
NEW QUESTION # 214
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: B
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIA™ Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Types of AI Learning Models"
NEW QUESTION # 215
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