PMI PMI-CPMAI日本語 認定試験の出題範囲:
| トピック | 出題範囲 |
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| トピック 1 | - 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.}
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| トピック 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.
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| トピック 3 | - 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.
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| トピック 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.
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| トピック 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.
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| トピック 6 | - 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.
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参照:https://www.pmi.org/certifications/ai-project-management-cpmai
PMI PMI-CPMAI日本語 試験概要:
| 認定ベンダー: | PMI |
| 試験名: | PMI Certified Professional in Managing AI (CPMAI) 試験 |
| 試験番号: | CPMAI |
| 試験形式: | 多肢選択式 |
| 対応言語: | English |
| サンプル問題: | PMI PMI-CPMAI日本語 サンプル問題 |
| 前提条件: | 公的な前提条件は設定されていません。PMIは、プロジェクト管理およびAIの概念に関する知識を推奨しています。 |