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PMI PMI-CPMAI日本語 Practice Q&A's

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PMI PMI-CPMAI日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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:

Question #1

AI/機械学習(ML)モデルの構成管理中に、チームは異なるテストデータセット間でパフォーマンス指標に一貫性がないことを確認した。
何がこの不整合問題を引き起こすのでしょうか?

  • A. 検査結果のばらつきが少ない
  • B. モデルの複雑性が不十分
  • C. トレーニングデータへの過学習
  • D. データ前処理手順が不適切
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

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

Correct Answer: C  🗳️

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Question #3

プロジェクトマネージャーは、財務予測のためのAIモデルの導入を監督する任務を負っている。
彼らは、モデルの予測が信頼できるものであることを確認する必要がある。
モデルのエラー率が許容範囲を超えた場合、次に何が起こるでしょうか?

  • A. 追加のAIモデルが使用されるため、人間の監視の必要性が軽減される
  • B. 予想よりも高い計算コスト
  • C. プロジェクトチームが問題を解決するというステークホルダーの信頼が高まる
  • D. モデルの再学習による運用開始の遅延
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

ある航空宇宙企業は、AIプロジェクトのデータ準備段階にある。プロジェクトチームは、モデル開発の可否を判断するために、データの品質を検証する必要がある。そのため、サンプリングレートの異なる複数のセンサーからのデータを統合する必要がある。
データの一貫性を確保する上で効果的な方法は何ですか?

  • A. リアルタイムデータ同期プロトコルの適用
  • B. センサーデータの集約
  • C. データ補間法の利用
  • D. カスタムデータ統合フレームワークの開発
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

航空宇宙製造プロジェクトにおいて、エンジニアたちは予測保守のためのAIシステムを訓練するためのデータ準備を行っている。モデル構築に先立ち、複数のセンサーから得られたデータを変換し、一貫性と正確性を確保する必要がある。
プロジェクトマネージャーは、こうした矛盾点をどのように処理すべきでしょうか?

  • A. データ拡張技術を用いて不足部分を補完する
  • B. 矛盾するデータポイントを特定し、調整する
  • C. センサーデータの検証プロトコルを実装する
  • D. 追加情報源を用いて現在のデータを強化する
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

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