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

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Updated: Oct 18, 2026

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PMI PMI-CPMAI日本語 Exam Overview:

Certification Vendor:	PMI
Exam Name:	PMI Certified Professional in Managing AI (CPMAI) Exam
Exam Number:	CPMAI
Exam Format:	Multiple choice
Available Languages:	English
Sample Questions:	PMI PMI-CPMAI日本語 Sample Questions
Pre Condition:	No formal prerequisite is publicly standardized; PMI recommends familiarity with project management and AI concepts.

PMI PMI-CPMAI日本語 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 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

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、AI/機械学習(ML)モデルの出力を改善するために、迅速なエンジニアリング手法を採用している。
彼らはまず大まかな質問から始め、それから具体的な要素を絞り込んでいった。もしチームが十分な背景情報を提供していなかったら、どのような結果になっていただろうか？

A. 出力にはより高い精度が含まれます。
B. このモデルはより創造的な成果を生み出すでしょう。
C. 回答は関連性に欠けるでしょう。
D. このモデルはより効率的に動作します。

Reveal Solution Discussion 0

Correct Answer: C 🗳️