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| Section | Objectives |
|---|---|
| Topic 1: AI Auditing Tools and Techniques | |
| Topic 2: AI Governance and Risk | |
| Topic 3: AI Operations |
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NEW QUESTION # 186
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: D
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIATM 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.
NEW QUESTION # 187
Which of the following BEST demonstrates "explainability by design"?
Answer: C
Explanation:
Explainability by design integrates interpretability considerations (model choice, feature transparency, explanation tooling) into development from the outset, rather than retrofitting explanations after deployment.
NEW QUESTION # 188
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?
Answer: B
Explanation:
Class imbalanceoccurs when one or more classes are underrepresented in the training data. The GREATEST concern ismodel bias(C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate intosystematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).
Data drift (A) refers to changes in data distributions over time-related but distinct. Data quality (B) is broader and may or may not be affected by imbalance. Overfitting (D) is a risk, but class imbalance more directly raises fairness and representativeness concerns rather than overfitting alone. Thus,bias arising from class imbalanceis the auditor's primary concern.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data balancing, bias risk).
ISACA AI ethics and model risk guidance discussing class imbalance and fairness.
NEW QUESTION # 189
Which of the following is the MOST important consideration for change management related to the organization-wide adoption of AI systems and tools?
Answer: C
Explanation:
Fororganization-wide adoptionof AI, the MOST important change management consideration is that the organization hassuitable data governance and infrastructure readiness(D). Without robust data governance, AI systems may rely on poor-quality, noncompliant, or insecure data; without appropriate infrastructure (compute, storage, monitoring, integration), implementations will be fragile, unreliable, and risky. AAIA stresses thatAI readinessincludes governance structures, data stewardship, and technical foundations.
Senior leadership involvement (A) is critical for sponsorship and culture, but even strong leadership cannot compensate for fundamentally inadequate data governance and infrastructure. Option B (shorter training cycles) focuses on user adoption but not core risk and control issues. Option C (phased implementation and stage gates) is good project discipline, but still depends on the underlying readiness. Therefore, from an AI risk and governance perspective, ensuringdata governance and infrastructure readinessis the primary prerequisite for successful, safe change management in AI adoption.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Governance of AI; AI Operations and Infrastructure.
ISACA guidance on AI readiness assessments and data governance as a precondition for AI transformation.
NEW QUESTION # 190
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:
Answer: D
Explanation:
Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIATM Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.
"To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods."
NEW QUESTION # 191
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