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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing AI | 8% | - Stakeholder management - Risk management in AI projects - Managing AI project teams and resources |
| Topic 2: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning - Deep Learning and Neural Networks |
| Topic 3: CPMAI Methodology | 41% | - Phase II: Data Identification & Curation
|
| Topic 4: Data for AI | 13% | - DataOps concepts - Data strategy and governance - Data preparation and preprocessing |
| Topic 5: Trustworthy AI | 9% | - Privacy and security - Ethical considerations and bias - Transparency and explainability |
| Topic 6: AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. In an aerospace manufacturing project, engineers are preparing data to train an AI system for predictive maintenance. They need to transform the data from multiple sensors and ensure it is consistent and accurate before building the model. What should the project manager do to handle the inconsistencies?
A) Enhance the current data with additional sources.
B) Use data augmentation techniques to fill the gaps.
C) Identify and reconcile conflicting data points.
D) Implement a validation protocol for sensor data.
2. A project team is trying to determine the most suitable environment to operationalize their AI/machine learning (ML) solution. They need to consider various factors to help ensure a successful implementation. What should the project manager do?
A) Evaluate the system's scalability options.
B) Identify the end users and their interactions.
C) Analyze the solution's compliance requirements.
D) Consider the cost of implementation.
3. An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?
A) Model bias
B) Data drift between environments
C) Hardware constraints
D) Underfitting
4. A project manager is considering the feasibility of an AI solution for a project aimed at improving customer service response times. They need to decide whether the project requirements can be solved with existing technologies or whether AI is the best approach. Which principle do the project manager's activities represent?
A) Identifying ethical concerns
B) Assessing scalability requirements
C) Determining automation potential
D) Evaluating cognitive alternatives
5. A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes patient information which is private. The project needs to comply with data privacy regulations in various countries. Which critical step should be performed to optimize representative training data?
A) Implement comprehensive bias detection metrics.
B) Increase the data set size without considering diversity.
C) Improve data understanding and preparation.
D) Enhance the key performance indicator (KPI) metrics.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: C |
