便利なAAIA認定テキスト - Fast2testのみ

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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勉強ガイド、AAIA日本語版参考資料

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ISACA Advanced in AI Audit 認定 AAIA 試験問題 (Q90-Q95):

質問 # 90
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

A. Completeness
B. Recall
C. Precision
D. Accuracy

正解：C

解説：
Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
"Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications." Accuracy and recall give broader insights, but only precision directly measures false positive risk.
Completeness is not a standard ML metric.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Performance Metrics for Classification Models"

質問 # 91
An organization plans to share customer data collected through an AI system with third-party vendors. Which of the following BEST demonstrates compliance with data privacy principles?

A. Communicating to customers about AI data sharing practices
B. Including a statement about AI data sharing practices in the company's privacy policy
C. Ensuring vendors implement adequate technical safeguards for data protection
D. Obtaining expressed consent from customers before sharing their data

正解：D

解説：
The strongest demonstration of compliance with privacy principles--especially those emphasized in AAIA, such as lawfulness, transparency, and consent--is obtaining expressed customer consent (B) before sharing data with third parties. Consent ensures that data processing aligns with legal and ethical requirements, reducing risks related to regulatory violations, unauthorized processing, and loss of customer trust.

質問 # 92
Which of the following should be applied to an AI system but are not typically used in traditional systems?

A. Controls to manage data governance
B. Controls to protect data privacy
C. Controls to monitor data poisoning
D. Controls to prevent data exfiltration

正解：C

解説：
AI systems face unique threats not commonly found in traditional IT environments, particularly data poisoning, where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning--such as input provenance checks, anomaly detection on training data, and integrity validation pipelines--are emphasized in AAIA's coverage of AI- specific vulnerabilities.

質問 # 93
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?

A. Daily precipitation and generator efficiency is 0.09
B. Rotational speed and tool wear is 0.56
C. Electricity demand and machine torque is 0.0
D. Temperature and generator effectiveness is -0.85

正解：D

解説：
A correlation of -0.85 (option C) represents a strong negative correlation, indicating a meaningful inverse relationship. In AI model development, high-magnitude correlations (positive or negative) strongly influence feature selection and model behavior.
AAIA emphasizes that auditors must understand correlation strength to evaluate:
* Feature relevance
* Model weight justification
* Risk of multicollinearity
* Data quality and representativeness
The other correlations are weak or negligible.
Thus, the pair with -0.85 is the most significant.
References:
AAIA Domain 1: AI Data Relationships
AAIA Domain 2: Data Understanding and Statistical Foundations

質問 # 94
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

A. AI change management requests
B. AI model inventory
C. AI model vulnerability assessment
D. AI acceptable use policy

正解：B

解説：
An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIA™ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.
"A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence." While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Evidence and Documentation of AI Strategy Alignment"

質問 # 95
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