更新された2025年12月21日 CPMAI_v7試験問題集でPDF問題とテストエンジン
最新（2025)PMI CPMAI_v7試験問題集
PMI CPMAI_v7 認定試験の出題範囲：
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質問 # 20
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?
- A. Lack of stakeholder support
- B. An incomplete milestone list
- C. Data Drift
- D. Model Drift
正解：C
解説：
A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
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質問 # 21
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?
- A. Use brute force method
- B. Use data from a previous project
- C. Use or extend a pre-trained model
- D. Use cloud-based technology
正解：C
解説：
The Transfer Learning task in Phase IV: Model Development recommends leveraging a pre-trained model as the starting point for a new, related task. By fine-tuning rather than training from scratch, teams dramatically reduce compute time and data requirements-ideal for tight iteration cycles.
質問 # 22
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)
- A. The algorithm code
- B. Supporting training data
- C. Hyperparameter settings
- D. Bias-variance tradeoff
正解：A、B、C
解説：
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
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質問 # 23
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?
- A. Apply every pattern to the project.
- B. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
- C. When in doubt, go with the Patterns & Anomalies pattern as all AI projects are about pattern matching.
- D. When in doubt, don't apply any pattern of AI.
正解：B
解説：
CPMAI's Task: AI Pattern Identification requires teams to map their specific business objectives to the most appropriate one or more of the Seven Patterns of AI. Starting from "what are we trying to accomplish?" and then selecting the pattern(s) that align with those goals is the prescribed approach.
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質問 # 24
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
- A. As an Augmented Intelligence system
- B. Used for Hyperpersonalization
- C. As a programmatic approach for automation
- D. As an autonomous system removing the human from the loop
正解：A
解説：
The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
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質問 # 25
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn't have access to all the data it needs. What's the best course of action?
- A. Cautiously move forward knowing you may need to pause mid-project which is ok.
- B. Move ahead as planned so you stay on time with your project.
- C. Do not move forward until you have access to all the data you need.
- D. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.
正解：C
解説：
During Phase I: Business Understanding, the Data Feasibility task explicitly mandates a Go/No-Go decision on data availability and access: "Do you have access to the data you need? If not, what do you need for access to the data? Mark as a 'NoGo.'" Projects should not proceed until all essential data access requirements are met to avoid wasted effort and unresolvable blockages down the line
質問 # 26
You are working for a large multinational organization and have been assigned to a new project. For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address? (Select all that apply.)
- A. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
- B. Securing data at rest
- C. Securely storing all data collected for training purposes
- D. Securing model data and metadata
正解：A、B、C、D
解説：
Under Domain VI: Trustworthy AI - Task 2: Implementing AI Privacy and Security, CPMAI mandates that teams must:
Apply data privacy principles and "ensure compliance with General Data Protection Regulation (GDPR)" and other relevant laws regardless of location .
Identify and protect Personally Identifiable Information (PII) and "develop comprehensive AI safety and security protocols," which encompasses securing both model data and metadata and enforcing security monitoring for production systems .
Implement best practices for data anonymization, defense against adversarial attacks, and the secure handling of datasets-this includes securing data at rest and securely storing training data in accordance with organizational and regulatory requirements .
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質問 # 27
When looking to implement AI to help break the Digital Transformation logjam, it's important to:
- A. Invest in technology solutions to solve the problem
- B. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes
- C. Start with basic automation and introduce AI only as needed
- D. Have the right culture for AI
正解：D
解説：
CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I's Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI-ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .
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質問 # 28
Which of the following best describes the technical definition of Machine Learning?
- A. The application of pre-defined rules and algorithms to solve complex problems.
- B. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
- C. The use of computing technology to enable machines to gain cognitive intelligence.
- D. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
正解：B
解説：
Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
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質問 # 29
You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?
- A. Generative AI solutions for content generation
- B. Decision tree and Random Forest approaches
- C. Image and object detection and recognition systems
- D. Autonomous patterns and process automation
正解：A
解説：
Generative AI is defined in the CPMAI Glossary as "AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data." Using Generative AI for content generation directly addresses the need to produce marketing images automatically.
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質問 # 30
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
- A. The location/source of your data collection
- B. The quality of the data you collect
- C. The makeup of your data team
- D. The volume of the data you collect
正解：C
解説：
In Phase II: Data Understanding, CPMAI's Generic Task Groups focus on:
Collecting initial data (identifying sources and volumes) and describing data (location/source) .
Verifying data quality to ensure completeness and correctness .
Team composition (the makeup of your data team) is addressed earlier under Phase I: Assess Situation, not during the Data Understanding phase where data requirements (quality, volume, source) are determined.
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質問 # 31
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?
- A. Predictive Analytics
- B. Diagnostic Analytics
- C. Projective Analytics
- D. Descriptive Analytics
正解：D
解説：
Descriptive analytics focuses on summarizing and reporting on past data-exactly what's needed to highlight historical purchasing patterns and performance metrics. The CPMAI Glossary defines analytics as "the use of statistical and computational methods to extract meaningful insights from data (related: predictive, descriptive, ...)" where descriptive analytics provides the retrospective view.
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質問 # 32
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time.
However, what's the one area you should not have the AutoML tool help with?
- A. Iterative modeling and evaluation
- B. Automatic model selection
- C. Automatic algorithm selection
- D. Automatic hyperparameter tuning
- E. Automatic model assessment
正解：A
解説：
CPMAI's Usage of AutoML task instructs teams to "Document how AutoML tools will be used for model creation" and to verify that the output can be integrated into the overall I/O flow . While AutoML excels at automating algorithm selection, model selection, hyperparameter tuning, and even preliminary performance metrics, CPMAI places iterative modeling and evaluation squarely under the manual Model Evaluation phase-where teams must interpret results against business success criteria and decide on next steps.
Entrusting that high-level, iterative decision-making to an AutoML black box would undermine the human- centric evaluation that CPMAI mandates.
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質問 # 33
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?
- A. Phase III
- B. Phase VI
- C. Phase II
- D. Phase I
- E. Phase V
- F. Phase IV
正解：C
解説：
Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .
質問 # 34
During which phase of an AI project should you consider Trustworthy AI considerations?
- A. Phase VI: Model Operationalization
- B. Every Phase of the AI project
- C. Phase II: Data Understanding
- D. Phase I: Business Understanding
正解：B
解説：
CPMAI's Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase-from Business Understanding through Operationalization-so that trustworthy practices are not an afterthought but a continuous, integrated activity .
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質問 # 35
Your team is testing the NLP model they just created to make sure it's performing as expected. Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?
- A. Nothing is wrong with this workflow. You can move to the next iteration
- B. Model Evaluation requires continuous model evaluation, retraining, and operationalization
- C. Team members should not be able to move to new projects until senior management signs off
- D. You need to make sure the AI Go/No Go questions have been addressed
正解：D
解説：
Phase V of the CPMAI v7 methodology-Model Evaluation and Maintenance-includes a formal CPMAI Phase V Go/No-Go assessment before any model can be moved into production or on to the next iteration.
This checkpoint ensures that the model meets predefined business success criteria, quality metrics, and risk considerations prior to deployment. Skipping this Go/No-Go review bypasses critical governance questions and undermines the integrity of the AI lifecycle.
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質問 # 36
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更新された検証済みの合格させるCPMAI_v7試験にはリアル問題と解答：https://www.jpntest.com/shiken/CPMAI_v7-mondaishu
最適な練習法にはPMI CPMAI_v7試験の素晴らしいCPMAI_v7試験問題PDF：https://drive.google.com/open?id=19lsyk9LyCXUXe2P3uh-p0T_TA6dvvEZF