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

  • Exam Code: PMI-CPMAI-JPN
  • Exam Name: PMI Certified Professional in Managing AI (PMI-CPMAI日本語版)
  • Updated: Oct 25, 2026
  • Q & A: 161 Questions and Answers

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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
  • 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 3
  • 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 4
  • 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.

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

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

Question #1

あるプロジェクトマネージャーは、AIソリューションの導入にあたり、さまざまなプロジェクト管理手法を検討している。
彼らは、そのアプローチが反復的な改善を可能にし、変化する要件に対応できるものであることを確認する必要がある。
この状況では、どちらのアプローチが効果的でしょうか?

  • A. インクリメンタル
  • B. ハイブリッド
  • C. 予測
  • D. 適応性/俊敏性
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

ある製造会社が、AIを活用した品質管理システムを運用開始しようとしている。プロジェクトマネージャーは、機密性の高い運用データを保護するという極めて重要な役割を担っているため、データプライバシーと規制遵守を確実に確保する必要がある。
これらの要件を満たす効果的な手法とは何でしょうか?

  • A. ストレージにハイブリッド暗号化方式を使用する
  • B. データセットにデータ匿名化を適用する
  • C. ネットワークセキュリティのためのゼロトラストアーキテクチャの実装
  • D. 安全なマルチパーティ計算フレームワークの活用
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

ある政府機関が、政策決定のために公共データを分析するAIシステムを使用している。プロジェクトマネージャーは、データの正確性、プライバシー、および不正使用に関連するリスクに対処する必要がある。この機関にとって最も大きなリスクは何か?

  • A. ユーザーデータは安全性の低いデータベースに保存されます。
  • B. このシステムには透明性プロセスが欠けています。
  • C. AIシステムは新しいデータで定期的に更新されていません。
  • D. AIシステムはサードパーティプロバイダーに依存しています。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

ある医療機関は、患者の再入院を予測するAIシステムを導入する計画を立てています。プロジェクトマネージャーは、患者の安全とデータの完全性を確保するためにリスク評価を実施する必要があります。AIシステムが倫理基準を遵守していることを確実にするための効果的な方法は何でしょうか?

  • A. 利害関係者影響分析の実施
  • B. 継続的な監視と監査の実施
  • C. 説明可能性フレームワークの使用
  • D. データ暗号化プロトコルの実装
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

AIソリューションの反復実装後、プロジェクトマネージャーは、メンテナンス要件が高いため、システムの拡張性に問題があることに気づきました。この問題に対処する効果的な方法は何でしょうか?

  • A. ルールベースのシステムに切り替えることで、メンテナンスの複雑さを軽減します。
  • B. 異なるシステムコンポーネントを分離するためにモジュール型アーキテクチャを採用する。
  • C. 生成型AIアプローチを取り入れて、モデルの更新を効率化する。
  • D. クラウドベースのソリューションを活用して、メンテナンスの拡張性を向上させる。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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