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PMI PMI-CPMAI日本語 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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

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

Question #1

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. アルゴリズムの偏りや公平性に関する懸念を見落とす
B. 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
C. 説明可能性要件に対する認識が限られている
D. ガバナンス戦略に関する詳細な計画の欠如

Reveal Solution Discussion 0

Correct Answer: D 🗳️