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PMI PMI-CPMAI日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identifying Data Needs for AI Projects (Phase II): This section of the exam measures the skills of a Data Analyst and covers how to determine what data an AI project requires before development begins. It explains the importance of selecting suitable data sources, ensuring compliance with policy requirements, and building the technical foundations needed to store and manage data responsibly. The section prepares candidates to support early data planning so that later AI development is consistent and reliable.
Topic 2
  • Testing and Evaluating AI Systems (Phase V): This section of the exam measures the skills of an AI Quality Assurance Specialist and covers how to evaluate AI models before deployment. It explains how to test performance, monitor for drift, and confirm that outputs are consistent, explainable, and aligned with project goals. Candidates learn how to validate models responsibly while maintaining transparency and reliability.}
Topic 3
  • The Need for AI Project Management: This section of the exam measures the skills of an AI Project Manager and covers why many AI initiatives fail without the right structure, oversight, and delivery approach. It explains the role of iterative project cycles in reducing risk, managing uncertainty, and ensuring that AI solutions stay aligned with business expectations. It highlights how the CPMAI methodology supports responsible and effective project execution, helping candidates understand how to guide AI projects ethically and successfully from planning to delivery.
Topic 4
  • Iterating Development and Delivery of AI Projects (Phase IV): This section of the exam measures the skills of an AI Developer and covers the practical stages of model creation, training, and refinement. It introduces how iterative development improves accuracy, whether the project involves machine learning models or generative AI solutions. The section ensures that candidates understand how to experiment, validate results, and move models toward production readiness with continuous feedback loops.
Topic 5
  • Operationalizing AI (Phase VI): This section of the exam measures the skills of an AI Operations Specialist and covers how to integrate AI systems into real production environments. It highlights the importance of governance, oversight, and the continuous improvement cycle that keeps AI systems stable and effective over time. The section prepares learners to manage long term AI operation while supporting responsible adoption across the organization.
Topic 6
  • Matching AI with Business Needs (Phase I): This section of the exam measures the skills of a Business Analyst and covers how to evaluate whether AI is the right fit for a specific organizational problem. It focuses on identifying real business needs, checking feasibility, estimating return on investment, and defining a scope that avoids unrealistic expectations. The section ensures that learners can translate business objectives into AI project goals that are clear, achievable, and supported by measurable outcomes.

Reference: https://www.pmi.org/certifications/ai-project-management-cpmai

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PMI Certified Professional in Managing AI (PMI-CPMAI日本語版) Sample Questions:

あるチームが機械学習プロジェクトに着手する準備を進めている。彼らはデータ準備パイプラインを構築する必要がある。チームメンバーの一人が、前回のプロジェクトで作成したパイプラインを再利用することを提案した。
この提案の何が問題なのでしょうか?

  • A. パイプラインはパターン要件に特化していますが、同じパターンであればパイプラインを再利用できます。
  • B. パイプラインは、パターンとモデルのニーズに特化しています。
  • C. パイプラインは、モデルの運用ニーズに特化しています。
  • D. プロジェクト間で必要に応じてパイプラインを再利用できるため、問題はありません。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

ある企業が、プロジェクトにAIを導入するかどうかを検討している。同社は事業目標を明確にし、使用したいAI機能を決定した。
プロジェクトマネージャーがプロジェクトを前進させるためには、どの行動が必要でしょうか?

  • A. 緊急時対応手順の特定
  • B. データ品質評価の実施
  • C. AIソリューションの試作版を実装する
  • D. ゴー/ノーゴー評価の実施
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

プロジェクトチームは、AIプロジェクトの次の段階への移行準備を進めている。チームは、透明性と説明可能性に関するすべての要件が満たされていることを確認する必要がある。
プロジェクトチームはどの活動を行うべきでしょうか?

  • A. 徹底的なデータ品質評価を実施する
  • B. AIモデルの意思決定プロセスを文書化する
  • C. 継続的な評価のためのフィードバックメカニズムを確立する
  • D. AIプロジェクトの倫理ガイドラインを定義する
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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このプロジェクトでは、複数の部門にまたがるAIシステムを統合する必要があり、各部門のアクセスレベルは異なります。この複雑なAIプロジェクトは、データ不正使用に関する重大な問題をプロジェクトマネージャーに突きつけています。プロジェクトチームは倫理ガイドラインを遵守するよう努めていますが、データ不正使用は依然として発生しています。さらに、このプロジェクトには地域ごとのデータ保護規制が関係しており、複雑さを一層高めています。
これらの課題を引き起こす原因は何でしょうか?

  • A. 説明可能性要件に対する認識が限られている
  • B. ガバナンス戦略に関する詳細な計画の欠如
  • C. データセキュリティのための堅牢な暗号化の実装の失敗
  • D. アルゴリズムの偏りや公平性に関する懸念を見落とす
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

プロジェクトマネージャーは、建築設計例を描画するための生成型AIモデルの開発に取り組んでいる。
プロジェクトマネージャーは、特定された分野専門家(SME)との効果的なコミュニケーションチャネルを確立する必要がある。
プロジェクトマネージャーは何をすべきでしょうか?

  • A. 外部コンサルタントへの依存度を減らすことで、プロジェクトコストを最小限に抑える。
  • B. 中小企業が建築設計の決定に責任を持つようにする。
  • C. ドメイン固有の課題を解決するための円滑なコラボレーションを可能にする。
  • D. 中小企業が最終的な建築設計の実施を監督できるようにする。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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