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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	監査ツールと手法：このセクションでは、AI監査人のスキルを評価し、適切なツールと手法を用いたAIシステムの監査に焦点を当てます。監査の計画と設計、AI特有のサンプリング手法、監査証拠の収集、品質保証のためのデータ分析の活用、フォローアップと品質管理措置を含むAI監査出力とレポートの作成などが含まれます。
トピック 2	AI 運用: 収集、品質、セキュリティ、分類などの AI 固有のデータニーズを管理し、設計段階からのプライバシーとセキュリティを備えた開発ライフサイクル方法論を適用し、変更とインシデントの管理、AI ソリューションのテスト、AI 関連の脅威と脆弱性の特定、AI の展開の監視を行います。
トピック 3	AI ガバナンスとリスク: さまざまな AI モデルとそのライフサイクルの理解、AI 戦略のガイド、役割とポリシーの定義、AI 関連のリスクの管理、データのプライバシーとガバナンスの監視、倫理的な慣行、標準、規制の遵守の確保などを網羅します。

>> AAIA参考書勉強 <<

ISACA AAIA全真問題集、AAIA試験対策書

AAIA学習ガイドでは、いつでもどこでも学習できます。学習時間を保証できない場合は、AAIA学習ガイドが最適です。随時学習し、学習に利用できるすべての時間を最大限に活用できるためです。オンライン版のAAIAラーニングガイドでは、デバイスの使用を制限していません。コンピューターを使用することも、携帯電話を使用することもできます。いつでも便利だと思うデバイスを選択できます。さらに、AAIA試験に問題なく合格できます。

ISACA Advanced in AI Audit 認定 AAIA 試験問題 (Q96-Q101):

質問 # 96
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Deploying two separate copies of the model after each adjustment to compare results
B. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
C. Assigning a single data science team member to adjust the model in order to establish accountability
D. Documenting model updates and retraining sessions to ensure traceability

正解：D

質問 # 97
A digital bank utilizes an AI system to generate credit scores. Which of the following would BEST mitigate the risk of sudden and unexplained changes in a borrower's credit score?

A. Obtaining and validating the credit scores from third-party agencies to cross-check AI-generated results
B. Using only data from the last six months to one year to avoid outdated information affecting the credit score
C. Ensuring the system is periodically reviewed and calibrated by human experts to maintain stability in predictions
D. Allowing the AI to operate fully autonomously to prevent processing delays

正解：C

解説：
Sudden and unexplained changes in AI-generated credit scores may result from data drift, model overfitting, or lack of recalibration. According to the AAIA™ Study Guide, regular expert review and calibration help maintain model reliability and transparency, particularly in high-stakes decisions like credit scoring.
"Ongoing human oversight ensures that predictive models remain stable and justifiable. In high-impact environments, such as banking, experts must review and recalibrate AI systems to prevent opaque or unexpected behavior." Option B may cause the exclusion of relevant long-term patterns. C promotes risk by removing oversight. D is a validation strategy, not a stability control. Therefore, A is the best option.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Monitoring and Recalibration Strategies"

質問 # 98
Which of the following sampling strategies would MOST likely involve the use of AI?

A. Adaptive
B. Judgmental
C. Statistical
D. Systematic

正解：A

解説：
" Adaptive sampling " is a dynamic strategy where the selection of future samples depends on the results of previous samples. AI excels in this area by continuously analyzing incoming data and automatically focusing the " sample " on areas where risks or anomalies are surfacing (e.g., a fraud detection bot increasing its scrutiny on a specific merchant after one suspicious transaction). The ISACA AAIA™ framework recognizes adaptive sampling as a significant advancement over static methods like systematic (Option C) or statistical (Option B) sampling because it uses real-time intelligence to improve audit coverage and resource allocation.

質問 # 99
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?

A. Establishing legally binding contracts for model use
B. Complying with documentation requirements
C. Understanding model intent and performance context
D. Identifying model vulnerabilities and training needs

正解：C

解説：
A "Model Card" is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear "Understanding of model intent and performance context." It allows the organization to determine if a vendor's model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition.

質問 # 100
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

A. Group analysis
B. Latency testing
C. Confusion matrix
D. Adversarial testing

正解：C

解説：
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing. According to the AAIA™ Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made-false positives and false negatives-thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed-not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Evaluation Metrics"

質問 # 101
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