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ハイパスレートのCPMAI_v7科目対策 &合格スムーズCPMAI_v7学習範囲 |検証するCPMAI_v7トレーニング
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PMI CPMAI_v7 認定試験の出題範囲：
トピック
出題範囲
トピック 1
- Machine Learning: This section is aimed at the Data
- AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.
トピック 2
- Data for AI: This domain targets the Data
- AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.
トピック 3
- CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.
トピック 4
- Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.
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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam 認定 CPMAI_v7 試験問題 (Q84-Q89):
質問 # 84
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. Image and object detection and recognition systems
- B. Decision tree and Random Forest approaches
- C. Autonomous patterns and process automation
- D. Generative AI solutions for content generation
正解：D
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 # 85
An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?
- A. Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer
- B. Train the model on GPUs
- C. Use a contractor to do the training portion
- D. Train the model over multiple desktop computers
正解：B
解説：
Training deep neural networks on CPUs is very slow. CPMAI's Glossary highlights that tensor processing units (TPUs) and GPUs are specialized hardware accelerators explicitly recommended to "accelerate the training and inference of machine learning models" by parallelizing the heavy matrix operations in neural- network layers. Switching from desktop CPU training to GPU-based training can reduce training time by orders of magnitude.
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質問 # 86
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?
- A. Phase IV
- B. Phase II
- C. Phase I
- D. Phase V
- E. Phase III
- F. Phase VI
正解：A
解説：
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.
質問 # 87
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 cloud-based technology
- D. Use or extend a pre-trained model
正解：D
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 # 88
Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?
- A. They did not practice model versioning and keep all versions of the model
- B. They did not have data governance in place
- C. They did not have a model retraining pipeline that took into account models
- D. They did not practice model iteration and properly iterate on the model
正解：A
解説：
In Phase VI: Model Operationalization of the CPMAI v7 methodology, project teams must explicitly plan for "model versioning and iteration" as part of deploying and maintaining models in production. Overwriting the original model without preserving its prior version prevents rollback and comparison, which is a core requirement for robust AI operations.
The Workbook states that operationalization considerations include "model versioning and iteration" to ensure that previous model artifacts are retained and that updates can be managed safely.
Additionally, under Edge Model Data Needs, teams are instructed to "Determine methods for model versioning and update" to support proper tracking and governance of model changes across iterations.
質問 # 89
......
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