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PMI PMI-CPMAI Exam Overview:

Certification Vendor:PMI (Project Management Institute)
Exam Name:PMI Certified Professional in Managing AI
Exam Number:PMI-CPMAI
Exam Duration:150 minutes
Available Languages:English
Exam Price:USD $520 for PMI members / USD $670 for non-members
Passing Score:Not publicly disclosed
Real Exam Qty:120
Certificate Validity Period:3 years
Related Certifications:PMI-PBA (Professional in Business Analysis)
PMP (Project Management Professional)
PMI-ACP (Agile Certified Practitioner)
Exam Format:Multiple Choice
Sample Questions:PMI PMI-CPMAI Sample Questions
Exam Way:Computer-based testing at PMI-authorized Pearson VUE test centers worldwide
Pre Condition:No mandatory prerequisites. However, basic project management knowledge (PMP or equivalent experience) is recommended. Secondary school diploma required if pursuing PMI membership.
Official Syllabus URL:https://www.pmi.org/certifications/certified-professional-managing-ai-cp-ai

>> PMI-CPMAIリンクグローバル <<

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教材をシミュレートするPMI-CPMAIのページでは、サンプルの質問であるデモを提供しています。デモを提供する目的は、お客様にトピックの私たちの部分を理解してもらうことと、それが開かれたときの学習資料の形式は何ですか？私たちの考えでは、これら2つのことは、PMI-CPMAI試験に関心のあるお客様が最も心配しているということです。製品ページにアクセスできるクリック可能なWebサイトであるソフトウェアを提供します。 PMI-CPMAI試験でマークされた赤いボックスはデモです。 PDFバージョンを無料でダウンロードでき、3つの形式すべてをクリックして表示できます。

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
  • 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 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.
トピック 3
  • 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 4
  • 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.
トピック 5
  • 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 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.

PMI Certified Professional in Managing AI 認定 PMI-CPMAI 試験問題 (Q69-Q74):

質問 # 69
A company is developing an AI-based recommendation system for its online retail platform.
During initial training, the team notices that the model ' s accuracy on the validation set is lower than expected. The team suspects the training data lacks diversity.
What should the project manager do?

正解：A

解説：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is A. Expand the input data to incorporate additional user behavior from a wider cross- section of customers.
According to PMI-CPMAI, data quality, representativeness, diversity, and suitability directly influence AI model development and evaluation.
A recommendation system trained on a narrow range of customer behaviors may fail to generalize to the broader customer population.
If the training data disproportionately represents particular customer segments, purchasing patterns, or product categories, the model may learn limited relationships that do not accurately reflect real-world behavior.
Expanding the dataset with relevant examples from a more diverse customer population can improve representation and potentially enhance model generalization.
The project manager should coordinate with data specialists to identify underrepresented segments, validate new data quality, and reassess performance using appropriate evaluation datasets.
Why the other options are incorrect:
- A: Correct because it directly addresses the suspected lack of training-data diversity.
- B: Increasing model complexity does not resolve inadequate data representation and may increase overfitting.
- C: Repeating identical records increases dataset size without introducing genuinely new information.
- D: Early stopping can help control overfitting but does not directly correct insufficient data diversity.
PMI-CPMAI emphasizes identifying data gaps, assessing representativeness, and iterating when evaluation results reveal shortcomings.
The team should also check whether the diversity problem affects particular customer groups or introduces unfair recommendations.


質問 # 70
An IT services company is verifying data quality for an AI project aimed at predicting server downtimes. The project manager needs to decide whether to proceed with data preparation.
Which technique should the project manager use?

正解：C

解説：
PMI-CPMAI emphasizes that data quality assessment must precede data preparation and modeling. The recommended technique at this stage is exploratory data analysis (EDA) to understand whether the data is fit for the AI use case. EDA allows the project team to examine distributions, detect missing values, outliers, noise, inconsistencies, data drift, and potential bias.
In the AI lifecycle view adopted by PMI, the data assessment step focuses on profiling data before investing effort in cleaning, transformation, or feature engineering. EDA gives insight into whether the available logs and telemetry (such as server performance metrics for downtime prediction) contain sufficient signal, appropriate time coverage, and consistent labeling to support reliable modeling. This aligns with PMI's guidance that project managers should "confirm that the dataset is adequate in completeness, accuracy, and relevance to the business objective before proceeding with preparation and modeling" (paraphrased from PMI AI data practices guidance).
Other options like data augmentation or advanced labeling are downstream enhancement techniques, and cost-benefit analysis is a management tool, not a data quality method. To decide whether to proceed with data preparation, the most suitable technique is exploratory data analysis (EDA).


質問 # 71
A project manager needs to address potential ethical concerns related to data misuse within a new AI system.
The AI system will handle large volumes of personal data. In addition, the project manager needs to ensure the data is used responsibly.
Which action should the project manager take?

正解：C

解説：
The best answer is B. Create a detailed data usage policy. In PMI's CPMAI framework, trustworthy AI requires more than technical security controls. It also requires clear rules for how data may be collected, accessed, shared, retained, and used responsibly, especially when personal data is involved. PMI's official exam content outline includes establishing governance protocols for personally identifiable information, monitoring regulatory and policy compliance, coordinating with legal and compliance teams, and ensuring privacy and secure handling across the AI lifecycle.
A detailed data usage policy directly addresses the core issue in the question: ethical concerns about misuse. It defines acceptable and unacceptable uses of personal data, clarifies accountability, and supports responsible behavior by everyone involved in the AI system. PMI's trustworthy AI guidance also emphasizes governance, responsibility, transparency, and ethics as foundational elements for building AI systems people can trust.
Option A is important, but access controls mainly restrict who can reach the data; they do not fully define responsible use. Option C is useful but too broad and ongoing rather than the most direct action. Option D improves visibility, but reporting alone does not prevent misuse. A clear data usage policy is the strongest first control for ethical and responsible data use.


質問 # 72
A telecommunications company is implementing an AI solution to optimize network performance. The project team needs to prepare the data for the AI system by addressing data format inconsistencies. Which method should the project manager use?

正解：B

解説：
PMI's CPMAI/PMI-CPMAI guidance places "data preparation and transformation" at the center of getting data into a usable state for model development and operations. The CPMAI v7 outline explicitly includes coordinating data preparation activities such as formulating data preparation requirements and performing data cleansing and enhancement-work that directly addresses inconsistent formats. In addition, CPMAI v7 lists "Executing Data Preparation and Transformation," including methods to improve data quality and accuracy and to clean/enhance data for optimal AI performance. When the issue is format inconsistency (e.g., mismatched schemas, units, encodings, timestamp formats), the PMI-aligned response is to define and execute the required transformation steps (normalize formats, standardize fields, convert units, align timestamps, encode categories) so the dataset meets the model and pipeline requirements. Governance (C) is important but is broader and slower-moving; it does not, by itself, resolve the immediate technical incompatibilities. A data quality report (D) documents problems but does not fix them. Data breach impact (B) is a different risk category. Therefore, the method that best meets the stated objective is determining the necessary data transformation steps.


質問 # 73
A project manager is working on the development of a generative AI model to draw architectural design examples.
The project manager needs to establish effective communication channels with identified subject matter experts (SMEs).
What should the project manager do?

正解：C


質問 # 74
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