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| Section | Objectives |
|---|---|
| Topic 1: AI Auditing Tools and Techniques | |
| Topic 2: AI Operations | |
| Topic 3: AI Governance and Risk |
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NEW QUESTION # 216
A retail organization uses an AI model to forecast inventory based on customer purchasing trends and updates the model quarterly. The model recently failed to recognize a surge in demand during a popular shopping season. Which of the following issues does this situation BEST demonstrate?
Answer: A
NEW QUESTION # 217
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?
Answer: D
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 # 218
Which of the following provides the BEST evidence that an organization's AI integration is aligned with its overall technology strategy?
Answer: C
Explanation:
The BEST evidence of alignment between AI integration and technology strategy is thatclear KPIs(A) have been defined for AI systems and tied to strategic objectives, measurable outcomes, and governance oversight.
KPIs demonstrate that AI initiatives are monitored, performance is evaluated, and outcomes align with enterprise value.
Policies (B) are foundational but do not demonstrate strategic alignment. Innovation culture (C) is broad and subjective. AI expertise (D) indicates capability, not alignment. AAIA stresses linking AI outcomes to business goals throughmetrics, monitoring, and governance dashboards.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Strategy Alignment.
NEW QUESTION # 219
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management's PRIMARY response?
Answer: A
Explanation:
A significantly higher rejection rate is a clear indicator of potentialalgorithmic discrimination. Management' s PRIMARY response should be to conduct acomprehensive bias analysis(C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help butonly afterthe root cause is identified-it is not the primary first step.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Bias, Fairness, and Transparency Evaluations.
NEW QUESTION # 220
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model ' s correlations?
Answer: B
Explanation:
While removing outliers can sometimes help with model performance, " Deleting outlier data values " arbitrarily poses the greatest risk to reliability because it can remove rare but legitimate data points that are critical for accurate pattern recognition. In domains like fraud detection or risk management, the outliers are the most important data. Distorting the natural distribution of data can lead to skewed correlations and a model that fails to recognize high-impact " tail " events. Encoding (Option B) and removing true duplicates (Option D) are standard, low-risk preprocessing steps. Correcting data types (Option C) is essential for enabling mathematical operations and is not a risk.
NEW QUESTION # 221
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