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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 |
| Trustworthy AI | 9% | - Privacy and security - Transparency and explainability - Ethical considerations and bias |
| AI Fundamentals | 16% | - AI capabilities and limitations - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence |
| Managing AI | 8% | - Risk management in AI projects - Managing AI project teams and resources - Stakeholder management |
| Data for AI | 13% | - DataOps concepts - Data preparation and preprocessing - Data strategy and governance |
| CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question 1
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?
A. Get your Users to Do it
B. Hire a Contractor Workforce
C. Find a source of already labeled data
D. Contract with Third Party Data Labeling Firms
Question 2
A manufacturing firm is planning to implement a network of intelligent machines to increase efficiency on the assembly line. The machines are equipped with advanced AI capabilities including precision assembly, quality control for predictive maintenance, and real-time data analysis. The intelligent machines should enhance operational efficiency, reduce downtime, and improve product quality. There needs to be seamless communication between the machines and existing systems, compliance with industry regulations, and a managed transition for the workforce. What is a beneficial outcome of using intelligent machines in this environment?
A. Higher investment costs without immediate returns
B. Over-reliance on technology leading to skill degradation
C. Scalability and flexibility in production
D. Increased vulnerability to cybersecurity threats
Question 3
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?
A. You are underfitting the data
B. You have selected the wrong algorithm
C. You have selected the wrong data
D. You are overfitting the data
Question 4
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
Question 5
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?
A. Data Scientist
B. Data Engineer
C. Citizen Data Scientist
D. Project Manager
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: A |
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