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AAIA New Braindumps - 100% Pass Quiz 2026 AAIA: ISACA Advanced in AI Audit First-grade Sample Questions
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ISACA AAIA Exam Syllabus Topics:TopicDetailsTopic 1
- 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.
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 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.
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ISACA Advanced in AI Audit Sample Questions (Q13-Q18):
NEW QUESTION # 13
An IS auditor identified that an AI model was optimized to approve more loans to meet growth targets, which led to a rise in defaults and expected losses. Which action will BEST address this situation?
- A. Require developers to adopt a model scorecard.
- B. Decrease the acceptance threshold used by the model.
- C. Align model reward function with long-term risk metrics.
- D. Add more recent loan application data to retrain the model.
Answer: C
Explanation:
This scenario describes a " misaligned objective, " where the AI is optimizing for a short-term goal (growth) at the expense of long-term stability (risk). In AI development, particularly in Reinforcement Learning or objective-based models, the " Reward Function " defines what the model considers " success. " To correct the issue, management must " Align the model reward function with long-term risk metrics, " such as default rates or profitability over time. This ensures the AI makes decisions that balance growth with risk appetite.
Simply adding data (Option B) or lowering thresholds (Option C) would only worsen the imbalance or hide the underlying misalignment.
NEW QUESTION # 14
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
- A. Placing numerical data into bins or buckets for a manageable quantity of correlations and result analyses
- B. Identifying and deleting duplicate entries in the data set
- C. Filling blank attributes in records with the mean, median, or mode within a grouping
- D. Separating multiple data attributes within one field into individual attribute columns
Answer: C
NEW QUESTION # 15
An IS auditor notes that an AI model achieved significantly better results on training data than on test data.
Which of the following problems with the model has the IS auditor identified?
- A. Generalization
- B. Bias
- C. Overfitting
- D. Underfitting
Answer: C
Explanation:
Overfitting occurs when a model performs very well on training data but poorly on unseen data, indicating that the model has learned patterns specific to the training set rather than generalizing effectively. The AAIA™ Study Guide identifies overfitting as a common problem that impacts model reliability.
"Overfitting limits the model's applicability to real-world scenarios. It reflects excessive tailoring to the training data and poor performance on new, diverse inputs." Underfitting (A) would result in poor performance on both training and test data. Generalization (C) is the desired state, and bias (D) is a separate issue. Therefore, B is correct.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Overfitting, Underfitting, and Generalization"
NEW QUESTION # 16
An IS auditor is performing an inventory audit for a manufacturing organization. Which of the following would BEST enable the auditor to identify types of products without assistance from organizational staff?
- A. Robotic process automation (RPA)
- B. Natural language processing
- C. Speech modeling
- D. Computer vision
Answer: D
Explanation:
Computer vision uses machine learning techniques to identify and classify visual data such as images or videos. In inventory audits, it can be used to recognize product types, scan barcodes, or evaluate storage conditions without human assistance.
"Computer vision is particularly effective in automated environments like manufacturing, where visual data from cameras or sensors can be processed to verify product identification and placement." NLP (A) and speech modeling (B) are not suitable for image-based tasks. RPA (C) automates tasks but cannot visually interpret products. Therefore, D is the correct tool.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "AI Tools in Operational and Inventory Audits"
NEW QUESTION # 17
An IS auditor is auditing a financial system in which a generative AI tool is used to identify trends in batches of 4,000 rows, while the generative AI tool has a limit of 3,000 tokens. Which of the following is the GREATEST concern?
- A. The AI output will be biased toward the first 3,000 tokens.
- B. The AI will prioritize high-value entries.
- C. The AI will reject the data set and not analyze the data.
- D. The AI will process only a portion of the data set.
Answer: A
NEW QUESTION # 18
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