実用的な-高品質なCPMAI日本語試験-試験の準備方法CPMAI過去問題

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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks
Trustworthy AI	9%	- Transparency and explainability - Ethical considerations and bias - Privacy and security
Managing AI	8%	- Managing AI project teams and resources - Risk management in AI projects - Stakeholder management
CPMAI Methodology	41%	- Phase II: Data Identification & Curation 1. Identify data needs 2. Assess data readiness and quality - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase III: Algorithm Selection & Model Training 1. Manage model training and configuration 2. Oversee AI/ML model technique selection - Phase VI: Iteration & Monitoring 1. Retraining and model drift management 2. Continuous monitoring and iteration - Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC
Data for AI	13%	- Data preparation and preprocessing - Data strategy and governance - DataOps concepts
AI Fundamentals	16%	- Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations

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CPMAI過去問題 & CPMAI日本語講座

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PMI Cognitive Project Management in AI (PMI-CPMAI) 認定 CPMAI 試験問題 (Q47-Q52):

質問 # 47
An AI model trained on historical transaction data performs well initially. Over time, customer behavior changes due to market conditions, causing declining prediction accuracy. The team observes a mismatch between training and current data distributions. What is this issue called?

A. Concept drift
B. Overfitting
C. Underfitting
D. Data leakage

正解：A

解説：
Concept drift occurs when data patterns change over time, degrading model performance.

質問 # 48
An insurance company is selecting an AI approach to automate simple claim approvals for low- risk cases. The organization wants the system to take actions with minimal human intervention based on predefined policies. Which AI capability best fits?

A. Autonomous systems
B. Conversational
C. Hyperpersonalization
D. Predictive analytics

正解：A

解説：
In PMI's Seven Patterns of AI, capability selection depends on whether the system is primarily advising humans or acting on their behalf. When the goal is to automate operational actions-- approving or routing claims under policy constraints with minimal human intervention--the capability aligns with autonomous systems, which emphasize automated execution within defined rules, safeguards, and operational boundaries. Predictive analytics (B) can score risk, but it typically supports decision support; autonomous systems extend this by taking actions automatically according to governance-approved policies. PMI-CPMAI's responsible and trustworthy AI principles reinforce that higher-autonomy use cases require stronger controls: clear escalation paths, contingency plans, monitoring, and audit trails to ensure accountability for automated decisions.

質問 # 49
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?

A. Model operations
B. Model Governance
C. Model discovery
D. Data operations

正解：B

質問 # 50
An aerospace firm is developing an AI system for predictive maintenance of their aircraft. The project team needs to define the required data to train the model.
Which activity should the project manager implement?

A. Setting up real-time data streaming from aircraft sensors
B. Conducting a pilot test with a small dataset
C. Implementing data cleaning and preprocessing routines
D. Developing a comprehensive data collection strategy

正解：D

質問 # 51
Before developing a complex model, a project team builds a simple rules-based version of the solution using existing business logic.
What is the primary purpose of this?

A. To reduce the amount of labeled data required
B. To establish a baseline that the AI model must outperform to justify its cost
C. To validate that the data pipeline can handle production volumes
D. To satisfy stakeholder requests for early delivery

正解：B

解説：
A baseline defines the performance level achievable without machine learning. If a complex model does not meaningfully exceed the baseline, the additional development, infrastructure, and maintenance cost cannot be justified.

質問 # 52
......

PMIさまざまな種類の候補者がCPMAI認定を取得する方法を見つけるために、多くの研究が行われています。シラバスの変更および理論と実践の最新の進展に応じて、CPMAIテストトレントを修正および更新します。 CPMAI認定トレーニングは、厳密な分析による近年のテストと業界動向に基づいています。したがって、お客様のCognitive Project Management in AI (PMI-CPMAI)のために、より多くの選択肢が用意されています。試験のためにCPMAI試験問題を選択することをお勧めします。

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