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PMI PMI-CPMAI日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • Managing Data Preparation Needs for AI Projects (Phase III): This section of the exam measures the skills of a Data Engineer and covers the steps involved in preparing raw data for use in AI models. It outlines the need for quality validation, enrichment techniques, and compliance safeguards to ensure trustworthy inputs. The section reinforces how prepared data contributes to better model performance and stronger project outcomes.
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.

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:

ある製造会社が、サプライチェーンを最適化するためにAIソリューションの導入を検討している。プロジェクトマネージャーは、このタスクにAIが必要かどうかを判断する必要がある。
どの対策が要件を満たすでしょうか?

  • A. サプライチェーンマネジメントで使用される非認知手法とAI手法の識別
  • B. サプライチェーン最適化のためのAIソリューションのスケーラビリティ評価
  • C. サプライチェーン内でAIが実行できる具体的な認知タスクを特定する
  • D. サプライチェーンにおけるAI導入の費用対効果比の評価
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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ある病院システムがチャットボットを導入したところ、エンドユーザーから苦情が寄せられた。エンドユーザーは人間と話していると思っているが、回答が意味不明なため不満を感じている。
エンドユーザーがAIチャットボットとやり取りしていることを確実に認識できるようにするには、透明性を確保するためにどのような点を考慮すべきでしょうか?

  • A. 解釈可能なAIモデルの使用
  • B. 使用ごとに開示通知
  • C. 多様なデータセットの包含
  • D. 高度なアルゴリズムを実用化する
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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ある通信会社が、AIを活用した顧客サポートシステムを導入しようとしている。プロジェクトマネージャーは、データ評価の監督を担当している。AIシステムが顧客からの問い合わせに対して正確かつ有益な回答を提供できることを確認する必要がある。
これらの目標達成を確実にするための効果的な方法は何ですか?

  • A. 複雑な顧客からの質問に対応するため、AIシステムと並行して静的ルールベースシステムを実装する。
  • B. 顧客サポート担当者のAIシステムへの理解を深めるため、定期的な研修を実施する。
  • C. AIシステムの知識ベースを、顧客とのやり取りから得られた最新情報やフィードバックで定期的に更新する
  • D. 顧客満足度調査を用いた四半期ごとの業績評価の実施
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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あるITサービス会社は、サーバーのダウンタイムを予測することを目的としたAIプロジェクトのために、データ品質の検証を行っている。プロジェクトマネージャーは、データ準備を進めるかどうかを決定する必要がある。
プロジェクトマネージャーはどの手法を用いるべきでしょうか?

  • A. データ拡張戦略
  • B. 詳細な費用便益分析
  • C. 探索的データ分析(EDA)
  • D. 高度なデータラベリング手法
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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ある金融サービス会社が、不正取引を検出するためのAIモデルを構築している。このモデルの成功には、データソースの特定と検証が不可欠である。
データの正確性を確保する上で効果的な方法は何ですか?

  • A. データリネージツールを活用して、データの起源と変換を追跡する
  • B. データクレンジングのためのバッチ処理システムの構築
  • C. 取引データのためのブロックチェーンベースの台帳の実装
  • D. 分散型データアクセスを実現するため、連合型データベースシステムを採用する。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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