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ISACA AAIA日本語 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Governance and Risk- AI Security and Controls
  • 1. Identity and access management
  • 2. Vendor and supply chain risk
  • 3. Threat and vulnerability management
- AI Governance Frameworks
  • 1. Regulatory and compliance considerations
  • 2. AI risk management
  • 3. AI policies and governance structures
Topic 2: AI Auditing Tools and Techniques- Audit Testing and Reporting
  • 1. Audit reporting and follow-up
  • 2. Evidence collection techniques
  • 3. Testing and sampling methodologies
- AI Audit Planning
  • 1. Audit resource planning
  • 2. AI audit methodologies
  • 3. Audit scoping and objectives
Topic 3: AI Operations- AI System Lifecycle
  • 1. Configuration and change management
  • 2. Monitoring and maintenance
  • 3. AI system development and deployment
- AI Data Management
  • 1. Training and testing data
  • 2. Data quality and integrity
  • 3. Operational AI controls

ISACA Advanced in AI Audit (AAIA日本語版) Materials That Go Where You Go

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The official ISACA Advanced in AI Audit (AAIA日本語版) outline splits its weight like this:
  • AI Operations ()
  • AI Governance and Risk ()
  • AI Auditing Tools and Techniques ()
Work the heaviest domains first, then let ExamPrepAway practice sessions confirm each one.
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Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
A professional team of specialized experts, organized specifically to research question patterns and study how different learners approach the AAIA日本語 material. Content is refined against the live ISACA Advanced in AI Audit (AAIA日本語版) blueprint, every answer is verified before publication, and the bank keeps pace with contemporary talent development — so it serves the way people actually prepare today, not the way they did years ago.

ISACA Advanced in AI Audit (AAIA日本語版) Sample Questions:

IS 監査人が生成 AI を使用してトランザクション データを格納する新しいデータベースの監査を計画する最も効果的な方法は次のうちどれですか。

  • A. データベース管理者 (DBA) とのインタビューの会議記録の要約
  • B. 技術固有のリスクと考慮事項の特定
  • C. データベースデータの変更における職務分離の競合を特定する
  • D. アーキテクチャ図の開発
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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ステージング環境で、AIシステムのパフォーマンスを事前定義された指標に基づいて測定する最も重要な理由は次のうちどれですか?

  • A. リリース前にAIシステムが要件を満たしていることを検証する
  • B. AIシステムが最新のデータセットでトレーニングされていることを確認する
  • C. AIシステムのユーザーインターフェースがアクセシビリティ基準に準拠していることを確認する
  • D. AIシステムが本番環境への展開前にプライバシー基準に準拠していることを確認する
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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AI モデルに取り込まれるデータがビジネス目標と一致するようにするための最良の方法はどれですか?

  • A. 新しいデータソースに変更
  • B. 予想される許容範囲内でデータを正規化する
  • C. データ入力要件を文書化する
  • D. 新しいデータ属性を定義する
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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監査担当者が現場視察を行った際、モデルの再トレーニングが承認プロセスなしに自動的に行われていることが判明しました。これはどのような最大のリスクをもたらすでしょうか?

  • A. モデルの学習時間が長くなる
  • B. 検証されていないモデル変更が本番環境に展開され、パフォーマンスの低下やバイアスの発生につながる可能性があります。
  • C. コンピューティングコストの増加
  • D. 説明可能性の低下
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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ある小売企業は、顧客の購買傾向に基づいて在庫を予測するAIモデルを活用し、四半期ごとにモデルを更新しています。しかし、このモデルは最近、繁忙期のショッピングシーズンにおける需要の急増を認識できませんでした。この状況は、以下のどの問題を最もよく示しているでしょうか。

  • A. トレーニングデータセットが小さいために過剰適合の問題が発生する
  • B. データセットの多様性がモデルのトレーニングに影響を与える
  • C. データドリフトがシステム予測に影響を与える
  • D. データの外れ値チェックが不足しており、予測精度に影響している
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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