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[Hardware] AAIA Dumps Torrent & AAIA Practice Questions & AAIA Exam Guide

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【Hardware】 AAIA Dumps Torrent & AAIA Practice Questions & AAIA Exam Guide

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ISACA AAIA Exam Syllabus Topics:
TopicDetails
Topic 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
  • 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.
Topic 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q110-Q115):NEW QUESTION # 110
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?
  • A. Reviewing the explainability of AI outputs
  • B. Assessing the computational cost of the model
  • C. Evaluating the diversity of the training data set
  • D. Analyzing the complexity of the algorithms used
Answer: A
Explanation:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance.
According to the ISACA AAIA™ Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability.
Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Transparency and Explainability"

NEW QUESTION # 111
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?
  • A. The solution documentation is still in draft.
  • B. The solution is hosted on a shared cloud environment.
  • C. The model has not been retrained recently.
  • D. The decision-making process is unexplainable.
Answer: D
Explanation:
TheGREATEST concernis when the AI system'sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.
Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).

NEW QUESTION # 112
Which of the following insider threats involving the use of AI would present the GREATEST risk?
  • A. Exfiltrating sensitive data
  • B. Launching social engineering attacks
  • C. Destroying system backups
  • D. Leaking of system hyperparameters
Answer: A
Explanation:
The GREATEST insider threat isexfiltrating sensitive data(D). AI systems often contain rich datasets including personal, financial, operational, and proprietary information. If an insider extracts or leaks this data, the result can be severe legal, regulatory, and reputational consequences.
Destroying backups (C) affects availability but not confidentiality. Social engineering attacks (B) are serious but indirect. Hyperparameter leakage (A) exposes model configuration but usually does not endanger sensitive data directly. AAIA stresses thatdata confidentiality risksare the most severe category in AI governance.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Risks; Data Protection and Confidentiality.

NEW QUESTION # 113
An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?
  • A. Data practices for training models
  • B. Accountability for data management
  • C. Portability of data
  • D. Retention of stored data
Answer: B
Explanation:
Accountability for data management (option D) is the most crucial consideration. The AAIA™ Study Guide emphasizes that "clear roles, responsibilities, and ownership for data management activities are central to trustworthy AI systems, as they ensure compliance, traceability, and the consistent application of policies and controls." Retention, portability, and data training practices are important, but accountability is foundational for the enforcement and monitoring of all other governance practices.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Accountability in Data Governance for AI"

NEW QUESTION # 114
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. Overfitting
  • C. Underfitting
  • D. Bias
Answer: B
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 # 115
......
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