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PMI PMI-CPMAI日本語 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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.}

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

ある医療機関が、患者の再入院を予測するAIモデル用のトレーニングデータを準備している。チームは、同じ診断名でも診療所間でコーディングに一貫性がないことを発見した。データ準備の段階で、この問題に対処するのに最適な方法はどれか？

A. 検証をスキップして時間を節約
B. 実データを合成データのみに置き換える
C. 矛盾は無視してください。モデルはいずれにせよパターンを学習します。
D. データ変換および標準化の手順を決定し、適用する

Reveal Solution Discussion 0

Correct Answer: D 🗳️