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ISACA AAIA 시험요강:

주제	소개
주제 1	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.
주제 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.
주제 3	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.

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최신 Advanced in AI Audit AAIA 무료샘플문제 (Q204-Q209):

질문 # 204
An organization is adopting AI for its procurement and inventory teams, raising concern from stakeholders that they will lose their jobs due to AI. Which of the following is the BEST way for the IS auditor to assess whether the potential negative impacts were minimized?

A. Review the AI roadmap for short-term and long-term milestones.
B. Review how the project management team collected feedback in engagement activities.
C. Review human-centered design practices to determine how they were considered.
D. Review the current state assessment of how AI may impact the organization.

정답：C

설명：
Human-centered design (HCD) focuses on integrating stakeholder needs, ethical implications, and social impact into AI system development. The AAIATM Study Guide emphasizes the role of HCD in minimizing negative workforce impacts and ensuring inclusive design.
"Auditors should review whether the AI system design process included human-centered practices-- particularly for applications affecting jobs or critical human roles. HCD ensures responsible adoption."

질문 # 205
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

A. Data transformation
B. Algorithm debugging
C. Model training
D. Problem framing

정답：D

설명：
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIATM Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.

질문 # 206
The PRIMARY benefit of using AI in audit fieldwork is that it:

A. Automates documentation of workpapers and artifacts.
B. Can scan large datasets to identify trends and anomalies.
C. Helps auditors to cover testing processes of all risk levels.
D. Saves time for the engagement manager in reviewing the audit work.

정답：B

설명：
According to the AAIA™ manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk-based approach. While automation of documentation (Option D) and time-saving for managers (Option A) are useful administrative advantages, they are secondary to the analytical capacity to uncover hidden patterns and improve the overall quality of audit fieldwork.

질문 # 207
Which of the following is the GREATEST risk associated with normalizing a data set before splitting it into training, testing, and validation sets?

A. The model affects data distribution.
B. The model requires external validation.
C. The developer uses tree-based models with the normalized data set.
D. The model gains indirect knowledge from data leakage.

정답：D

설명：
Data normalization involves scaling data (e.g., ensuring all values are between 0 and 1). If you normalize the entire dataset before splitting it, the " Training Set " will be influenced by information from the " Testing Set " (such as the global maximum and minimum values). This is a form of " Data Leakage. " According to the AAIA™ manual, this " indirect knowledge " makes the model ' s test performance appear much better than it actually is, leading to a false sense of security. The correct procedure is to split the data first , then calculate normalization parameters using only the training data and apply those parameters to the test data.

질문 # 208
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. Underfitting
B. Generalization
C. Overfitting
D. Bias

정답：C

설명：
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"

질문 # 209
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