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PMI PMI-CPMAI Exam Overview:

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

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PMI-CPMAI試験の準備方法 | 実用的なPMI-CPMAI日本語版トレーリング試験 | 実際的なPMI Certified Professional in Managing AI試験関連赤本

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PMI PMI-CPMAI 認定試験の出題範囲：

トピック	出題範囲
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.}
トピック 2	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.
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.
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.
トピック 5	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.
トピック 6	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.

PMI Certified Professional in Managing AI 認定 PMI-CPMAI 試験問題 (Q82-Q87):

質問 # 82
To determine if an AI solution is appropriate for an upcoming project, the project manager needs to evaluate whether the project requires a cognitive approach.
What should the project manager address?

A. Estimated project cost
B. Existing well-defined business objectives
C. Required level of interpretability
D. Potential non-cognitive alternatives

正解：D

解説：
The best answer is D. Potential non-cognitive alternatives . In PMI-CPMAI, the early business assessment is not just about deciding whether AI can be used, but whether AI should be used at all for the problem.
Under Identify Business Needs and Solutions , PMI's official exam content outline explicitly states that initial AI feasibility includes comparing AI approaches against traditional solution alternatives . That means the project manager should first determine whether a simpler, rules-based, workflow, reporting, or conventional software solution could solve the problem without introducing unnecessary AI complexity, risk, cost, or governance burden.
This also aligns with your uploaded CPMAI-aligned playbook, which emphasizes that teams should avoid applying AI automatically and should choose governance and solution rigor proportionate to the actual need and risk. The playbook repeatedly stresses that the right decision starts with the business problem and whether AI is truly the appropriate approach, rather than assuming an AI solution by default.
Why the others are weaker: business objectives matter, cost matters, and interpretability may matter later, but the key question for deciding whether a cognitive approach is appropriate is whether viable non-cognitive alternatives already exist. That is the clearest PMI-CPMAI-aligned choice.

質問 # 83
An AI project team needs to consider compliance with data regulations and explainability standards as requirements for a new AI solution.
At what point in the project should the requirements be approached?

A. As part of the business understanding phase
B. As part of the final testing phase
C. As optional guidelines based on project scope
D. As part of the data preparation phase

正解：A

解説：
In PMI-CP/CPMAI-aligned practice, compliance requirements such as data protection regulations (e.g., privacy laws, data residency) and explainability standards are treated as business and regulatory constraints, not as late technical details. They must therefore be identified and incorporated during the business understanding phase. At this stage, the project manager and stakeholders clarify the problem statement, success criteria, risk appetite, and constraints under which the AI solution must operate. That includes explicitly stating: which regulations apply, what level of transparency or explainability is required, which stakeholders must be able to understand model outputs, and which decisions must remain under human control.
By capturing these requirements early, they directly influence the choice of AI pattern, model families, data sources, architecture, and governance mechanisms. If these constraints are postponed until data preparation or final testing, the team risks discovering that the chosen models are too opaque, the data cannot legally be used as collected, or additional documentation and controls are needed that fundamentally change scope and timeline. CPMAI stresses that responsible AI and regulatory compliance are "built in from the beginning," so the correct point to approach these requirements is the business understanding phase.

質問 # 84
A government agency is planning to implement a new AI-driven public service system. The project manager needs to develop a business case to secure funding. The agency's goals are to improve service delivery and reduce response times.
Which method will provide the results that meet the project manager's objective?

A. Analyzing case studies from other agencies
B. Conducting a pilot program
C. Holding stakeholder workshops
D. Creating a detailed ROI projection

正解：B

解説：
Within the PMI-CPMAI guidance, developing a strong business case for AI requires evidence-based justification that the proposed solution will deliver measurable value, not just theoretical benefits. For a government agency whose stated goals are improving service delivery and reducing response times, the most convincing way to support a funding request is to demonstrate these improvements in a realistic environment.
A pilot program or proof-of-concept allows the project team to implement the AI-driven public service system on a limited scale, collect operational data, and compare key performance indicators (KPIs) such as response time, throughput, user satisfaction, and error rates before and after AI adoption.
PMI-CPMAI emphasizes that pilots help validate assumptions about feasibility, scalability, and stakeholder acceptance while revealing hidden risks and integration issues early. They provide concrete, context-specific metrics that can be used directly in the business case, strengthening arguments around public value, efficiency gains, and cost-effectiveness. By contrast, case studies and workshops are indirect and qualitative, and ROI projections alone remain hypothetical without empirical evidence. Therefore, conducting a pilot program best meets the project manager's objective of producing robust, measurable results that support a compelling AI business case for funding approval.

質問 # 85
An aerospace company is integrating AI for predictive maintenance. The project manager is concerned about potential delays due to external dependencies.
Which initial step should the project manager take?

A. Increase resource allocation
B. Engage with multiple suppliers
C. Establish contingency plans
D. Implement just-in-time inventory

正解：B

質問 # 86
Clean and well-labeled datasets used for machine learning (ML) are being partitioned into three subsets:
training sets, validation sets, and test sets.
As the project team is working on these activities, what is an efficient way to split the data?

A. Use the same data for all sets.
B. Split by patterned subsampling.
C. Split by alphabetical order.
D. Split by random subsampling.

正解：D

解説：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is A. Split by random subsampling.
In machine learning, datasets are commonly separated into training, validation, and testing subsets to support reliable model development and evaluation.
The training set is used to fit the model. The validation set supports model selection, parameter tuning, and performance assessment during development. The test set provides a final independent evaluation of the selected model.
Random subsampling can create representative subsets while reducing selection bias caused by arbitrary ordering.
For independently sampled data, random splitting is often an effective baseline approach. However, the selected splitting strategy must reflect the structure of the dataset.
For example, stratified random sampling may be preferable when class distributions are imbalanced. Time- based splitting is generally more appropriate for temporal prediction problems, while group-aware splitting helps prevent the same patient, customer, or entity from appearing in both training and testing datasets.
Why the other options are incorrect:
- A: Correct as a standard general-purpose splitting approach.
- B: Reusing the same observations across sets causes data leakage and unreliable evaluation.
- C: Alphabetical order has no general statistical justification.
- D: Patterned subsampling can introduce systematic selection bias.
PMI-CPMAI emphasizes reliable evaluation and preventing misleading model performance assessments through appropriate data preparation and validation.

質問 # 87
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PMI-CPMAI試験関連赤本: https://www.passtest.jp/PMI/PMI-CPMAI-shiken.html

ちなみに、PassTest PMI-CPMAIの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=12LhueRiW_7MFTiutraOUE9az48hIhWgK