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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
| Data for AI | 13% | - DataOps concepts - Data strategy and governance - Data preparation and preprocessing |
| Trustworthy AI | 9% | - Ethical considerations and bias - Privacy and security - Transparency and explainability |
| AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| CPMAI Methodology | 41% | - Phase II: Data Identification & Curation
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
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 satisfy stakeholder requests for early delivery
- 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 reduce the amount of labeled data required
Correct Answer: B 🗳️
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
You have been tasked at your organization to manage a large language model (LLM) project.
Identify what LLMs are useful for. (Choose all that apply.)
- A. Process automation
- B. Code generation
- C. Machine Translation
- D. Classify and categorize content
- E. Text summarization
- F. Improve search quality
Correct Answer: B,C,D,E,F 🗳️
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs. What's the most critical role to staff for in the Big Data / Data Engineering Environment?
- A. Data Engineering
- B. Senior management
- C. Data Engineering and Data Scientists
- D. All roles are critical to staff in the Four different AI Tech environments
- E. Data Scientists
Correct Answer: A 🗳️
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
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. Conducting a SWOT (strengths, weaknesses, opportunities, threats) analysis to evaluate project resources
- B. Applying the theory of constraints to identify and mitigate bottlenecks in resource allocation
- C. Utilizing a resource-leveling technique to balance workloads across all team members
- D. Implementing a Monte Carlo simulation to predict resource allocation and project timelines
Correct Answer: B 🗳️
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
A project manager is leading a complex project for a global financial institution. The project is developing an AI-driven system for real-time fraud detection and risk management. The system needs to adhere to all financial regulations. The project manager has identified skills gaps with the existing available resources. What should the project manager do?
- A. Delay the project until internal expertise is developed.
- B. Engage consultants to fill the expertise gap.
- C. Allocate additional budget for consultant AI training.
- D. Proceed with the project until external expertise is needed.
Correct Answer: B 🗳️
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
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