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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q76-Q81):

NEW QUESTION # 76
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
What role would be most suited for this?

Answer: C


NEW QUESTION # 77
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?

Answer: C


NEW QUESTION # 78
A finance company is planning an AI project to improve fraud detection. The project manager has identified multiple cognitive patterns that can be used. Which method will narrow the project scope?

Answer: A

Explanation:
Prioritizing patterns based on potential impact and complexity helps narrow the scope by selecting the cognitive patterns that provide the most value while remaining feasible to implement. This keeps the fraud detection project focused and avoids unnecessary complexity from trying to use every identified pattern.


NEW QUESTION # 79
A logistics company is operationalizing an AI solution to optimize delivery routes. The project manager needs to gather up-to-date information on traffic patterns, delivery schedules, and vehicle performance. Which method will integrate these diverse data types?

Answer: A

Explanation:
Implementing a real-time data processing framework supports continuous ingestion and integration of current traffic patterns, delivery schedules, and vehicle performance data. This enables the route optimization system to use timely, diverse operational data for accurate decision-making.


NEW QUESTION # 80
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

Answer: C


NEW QUESTION # 81
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