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PMI Certification PMI-CPMAI日本語 Braindumps

PMI-CPMAI日本語

Exam Code: PMI-CPMAI-JPN

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

Updated: Nov 05, 2026

Q & A: 161 Questions and Answers

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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
  • 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 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.

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
あるプロジェクトチームは、顧客離脱を予測するAIモデルのデータ評価を監督している。彼らは、モデルの予測が特定の顧客層に偏っていることに気づいた。
このバイアスを軽減するための効果的な手法は何ですか?

A. ハイパーパラメータの調整
B. バッチサイズを増やす
C. 層化抽出法の実施
D. 合成データ生成を用いる


Question #2
プロジェクトチームは、将来の監査のために、AI関連のあらゆる決定と行動を包括的に文書化する責任を負っています。彼らは、特定のAI選択の理由、その影響、および実装中に発生したあらゆる問題を追跡する必要があります。
この状況は何を象徴しているのでしょうか?

A. 戦略的整合性
B. 透明性
C. 業務効率
D. コンプライアンス管理


Question #3
ある製造会社は、組立ラインの効率向上を目指し、インテリジェントマシンのネットワーク導入を計画している。これらのマシンには、精密組立、予知保全のための品質管理、リアルタイムデータ分析など、高度なAI機能が搭載されている。インテリジェントマシンは、運用効率の向上、ダウンタイムの削減、製品品質の向上に貢献することが期待される。そのためには、マシンと既存システム間のシームレスな通信、業界規制への準拠、そして従業員の円滑な移行が不可欠となる。
この環境において、知能機械を使用することによる有益な成果は何でしょうか?

A. 生産における拡張性と柔軟性
B. 技術への過度な依存がスキルの低下につながる
C. 投資コストは高いが、すぐにリターンは得られない。
D. サイバーセキュリティの脅威に対する脆弱性の増加


Question #4
ある金融機関が、不正検出のための新しいAIシステムを導入しようとしている。プロジェクトチームは、データの品質、完全性、関連性を検証することで、データがAIソリューションの要件を満たしていることを確認する必要がある。彼らは、様々な内部および外部データソースにアクセスできる。
プロジェクトチームの目標に合致する手法はどれですか?

A. 包括的なデータ監査およびデータクレンジングプロセスの実施
B. 複雑さを避けるために、データソースを内部データベースに限定する
C. 改善チェックなしでデータを統合し、プロジェクトのタイムラインを短縮する
D. 特定のデータに合わせて調整せずに事前学習済みモデルを使用する


Question #5
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. データ品質評価の実施
B. ゴー/ノーゴー評価の実施
C. AIソリューションの試作版を実装する
D. 緊急時対応手順の特定


Solutions:

Question #1
Correct Answer: D
Question #2
Correct Answer: B
Question #3
Correct Answer: A
Question #4
Correct Answer: A
Question #5
Correct Answer: B

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