PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Managing AI | 8% | - Risk management in AI projects - Stakeholder management - Managing AI project teams and resources |
| Trustworthy AI | 9% | - Ethical considerations and bias - Privacy and security - Transparency and explainability |
| Machine Learning | 13% | - Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks |
| AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - Types of AI and Machine Learning - AI capabilities and limitations |
| Data for AI | 13% | - Data preparation and preprocessing - Data strategy and governance - DataOps concepts |
| CPMAI Methodology | 41% | - Phase IV: Model Evaluation
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A telecommunications company is adopting an AI-based customer service chatbot. They are concerned about potential quality issues affecting customer satisfaction. What should the project manager do?
A) Set up a dedicated team to monitor and address quality issues
B) Conduct regular performance reviews and updates based on customer feedback
C) Develop a comprehensive quality assurance plan for the chatbot
D) Initiate a beta testing phase with a small group of customers
2. You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Choose all that apply.)
A) Automation skills, especially around creating RPA bots
B) Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
C) Prompt engineering skills
D) Understanding of algorithms
E) Strong math skills, especially in calculus and statistics
F) Critical thinking skills
3. A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?
A) Perform a comprehensive hyperparameter tuning.
B) Set up cross-validation with a larger dataset.
C) Establish continuous monitoring and feedback loops.
D) Implement additional data augmentation techniques.
4. An AI model is frequently retrained with new data to improve performance. However, stakeholders report inconsistent predictions across releases, causing confusion and reduced trust in the system. What risk is most likely occurring in this situation?
A) Data leakage
B) Bias elimination
C) Model instability
D) Underfitting
5. An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?
A) Lines of code written
B) Number of features delivered
C) User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
D) Response time only
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B,C,D,E,F | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: C |
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