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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) |
| Topic 2: Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| Topic 3: Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| Topic 4: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
| Topic 5: Managing AI | 8% | - Stakeholder management - Managing AI project teams and resources - Risk management in AI projects |
| Topic 6: CPMAI Methodology | 41% | - Phase V: Operationalization
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A project manager is preparing a contingency plan for an AI-driven customer service platform.
They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager's objective?
A) Implementing a manual override system for critical customer queries
B) Developing an automated fallback chatbot with limited capabilities
C) Providing extensive training to customer service representatives on handling AI failures
D) Creating a robust customer service logging system to quickly identify and resolve issues
2. A healthcare project manager is working on implementing an AI-driven diagnostic tool. The project requires identifying key resources, including data scientists, server infrastructure, and external AI consultants. The project manager needs to ensure the allocated resources are optimized for the project's success. Which method will help to ensure the optimal allocation of resources?
A) Utilizing a resource-leveling technique to balance workloads across all team members
B) Implementing a Monte Carlo simulation to predict resource allocation and project timelines
C) Conducting a SWOT (strengths, weaknesses, opportunities, threats) analysis to evaluate project resources
D) Applying the theory of constraints to identify and mitigate bottlenecks in resource allocation
3. An organization aims to improve transparency and trust in its AI systems. Stakeholders request documentation outlining model purpose, limitations, risks, and performance characteristics. Which artifact should the project manager ensure is created and maintained?
A) Deployment script
B) Source code repository
C) Model card
D) Data pipeline diagram
4. A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?
A) Move forward cautiously with the understanding that there may be a need for a pause mid-project.
B) Move forward in order to remain on schedule with the project.
C) Do not move forward until access is given to all the necessary data.
D) Move forward while anticipating data access is given when needed. An iterative approach provides the ability to return to steps as needed later on.
5. A supervised learning project requires 50,000 labeled images. The subject matter is highly specialized, and the organization has three qualified domain experts available part time.
What is the most significant project risk the manager should plan for?
A) Model training will require additional computing capacity
B) Labeling throughput will constrain the project schedule
C) Domain experts may disagree with the model's predictions
D) The images will require format standardization
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |



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