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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Machine Learning13%- Algorithms and models (e.g., NLP, Computer Vision)
- Deep Learning and Neural Networks
- Supervised, Unsupervised, and Reinforcement Learning
Topic 2: Managing AI8%- Risk management in AI projects
- Managing AI project teams and resources
- Stakeholder management
Topic 3: Data for AI13%- DataOps concepts
- Data strategy and governance
- Data preparation and preprocessing
Topic 4: Trustworthy AI9%- Transparency and explainability
- Ethical considerations and bias
- Privacy and security
Topic 5: AI Fundamentals16%- Concepts and terminology of Artificial Intelligence
- Types of AI and Machine Learning
- AI capabilities and limitations
Topic 6: CPMAI Methodology41%- Phase IV: Model Evaluation
  • 1. Evaluate model performance against metrics
  • 2. Oversee AI/ML model QA/QC
- Phase V: Operationalization
  • 1. Define success criteria and monitoring
  • 2. Plan for deployment and integration
- Phase I: Problem Identification
  • 1. Evaluate initial AI feasibility
  • 2. Identify business needs and solutions
- Phase III: Algorithm Selection & Model Training
  • 1. Manage model training and configuration
  • 2. Oversee AI/ML model technique selection
- Phase VI: Iteration & Monitoring
  • 1. Continuous monitoring and iteration
  • 2. Retraining and model drift management
- Phase II: Data Identification & Curation
  • 1. Assess data readiness and quality
  • 2. Identify data needs

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PMI Cognitive Project Management in AI (PMI-CPMAI) 認定 CPMAI 試験問題 (Q149-Q154):

質問 # 149
A financial services firm is assessing the success of a newly operationalized AI system for fraud detection. The project manager needs to evaluate the model against business key performance indicators. (KPIs). What is an effective method to help ensure the accuracy of this evaluation?

正解：B

解説：
Utilizing a diverse set of validation techniques improves evaluation accuracy by checking the AI fraud detection system from multiple perspectives, such as model performance, business KPI alignment, false positive and false negative rates, and operational impact. This gives a more reliable assessment than relying on one metric or broad financial reporting alone.


質問 # 150
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

正解：C

解説：
For successful hyperpersonalization, individual profiles must both learn and adapt continuously while being programmed for various purposes to deliver dynamic, personalized experiences.


質問 # 151
When building your model, you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective. At what phase of CPMAI is this most appropriate to do this?

正解：D

解説：
Phase V of CPMAI focuses on model evaluation, including validating that the model meets business objectives and performs as expected before deployment.


質問 # 152
An AI project team needs to assess the feasibility of an AI solution. In order to proceed with the AI project, what should the project manager address?

正解：D

解説：
Alignment with business objectives is essential for determining whether the AI solution is feasible and worth pursuing. The project should proceed only when the AI approach supports the intended business outcomes, success criteria, and organizational value.


質問 # 153
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

正解：C

解説：
Real-time prediction enables continuous, instant model inference required for monitoring machinery, providing timely alerts and decision support without delay.


質問 # 154
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