ISACA AAIA赤本合格率: ISACA Advanced in AI Audit - MogiExam合格のを助ける

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ISACA AAIA 認定試験の出題範囲：

トピック出題範囲
トピック 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.
トピック 2
  • 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.
トピック 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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AAIA試験問題、AAIA模擬練習

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ISACA Advanced in AI Audit 認定 AAIA 試験問題 (Q245-Q250):

質問 # 245
Which of the following is the MOST important risk for an IS auditor to consider when reviewing the adoption of an AI system?

正解：B

解説：
Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIA™ Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.
"Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place." While costs (A) and industry maturity (B) are considerations, they do not pose the same systemic ethical risk.
Resistance (D) is a change management issue. C represents the most impactful and widespread risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias and Fairness in AI"


質問 # 246
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

正解：A


質問 # 247
The PRIMARY purpose of utilizing neural networks in AI is to:

正解：A

解説：
Neural networks are designed to mimic the way the human brain processes information, enabling AI systems to identify complex patterns and make decisions based on data inputs. The AAIA™ Study Guide defines neural networks as central to deep learning architectures that emulate cognitive functions.
"Neural networks simulate the interconnectivity and learning processes of the human brain. This capability enables AI systems to handle unstructured data and perform tasks such as image recognition, natural language processing, and predictive analytics." Options A, B, and D reflect ancillary benefits but not the core design purpose of neural networks. Thus, C is the most accurate.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Neural Networks and Deep Learning"


質問 # 248
An IS auditor is assessing whether an organization's AI risk taxonomy is complete. Which risk category is MOST often overlooked compared to traditional IT risk taxonomies?

正解：D

解説：
Traditional IT risk taxonomies (CIA triad) do not natively capture AI-specific risks such as model bias, drift, and explainability failures, which require an extended taxonomy.


質問 # 249
The internal audit department of a large law firm plans to implement an AI solution to review and analyze contracts stored in its enterprise resource planning (ERP) system. Which of the following AI technologies is MOST suitable for this requirement?

正解：C

解説：
Natural Language Processing (NLP) is the specific branch of AI focused on the interaction between computers and human language. For tasks involving the review, summarization, and extraction of insights from unstructured text documents like legal contracts, NLP is the most suitable technology. It allows the system to understand semantic meaning, identify clauses, and detect inconsistencies within a legal context.
While NLP often utilizes Deep Learning (Option A) and Neural Networks (Option B), those are the underlying " architectures, " whereas NLP is the specialized " application " required for document analysis.
ML (Option D) is a broad category that includes NLP but lacks the specificity required for linguistic processing.


質問 # 250
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