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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Fundamentals | 16% | - Types of AI and Machine Learning - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations |
| Topic 2: Managing AI | 8% | - Managing AI project teams and resources - Stakeholder management - Risk management in AI projects |
| Topic 3: CPMAI Methodology | 41% | - Phase V: Operationalization
|
| Topic 4: Data for AI | 13% | - Data preparation and preprocessing - DataOps concepts - Data strategy and governance |
| Topic 5: Trustworthy AI | 9% | - Transparency and explainability - Privacy and security - Ethical considerations and bias |
| Topic 6: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?
A. Model Operationalization
B. Model retraining
C. Model Drift
D. Model reevaluation
Question #2
An IT services company is integrating an AI solution to automate its customer service functions.
The integration team is facing resistance from the customer's employees. Which action should the project manager perform to manage this risk?
A. Offer the option to join another team.
B. Implement a gradual phased rollout.
C. Mandate immediate transition from management.
D. Conduct all-hands meetings on the benefits.
Question #3
A telecommunications company is considering an AI solution to improve customer service through automated chatbots. The project team is assessing the feasibility of the AI solution by examining its potential scalability and effectiveness. What will present the highest risk to the company?
A. The solution might breach customer data privacy regulations, leading to legal consequences.
B. The chatbot may not integrate well with existing customer service platforms.
C. The team may lack experience implementing AI-based customer service solutions.
D. The solution may not handle the volume of customer queries effectively.
Question #4
A financial institution is implementing a new AI system for fraud detection. The project team must ensure the data meets the needs of the AI solution by verifying data quality, completeness, and relevance. They have access to various internal and external data sources. Which method addresses the project team's objectives?
A. Using pretrained models without tailoring to specific data
B. Integrating data without improvement checks to expedite the project timeline
C. Limiting the data sources to internal databases to avoid complications
D. Conducting a comprehensive data audit and cleansing process
Question #5
Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?
A. Recognition Pattern
B. Predictive Analytics & Decision Support
C. Goal Driven Systems
D. Predictive Analytics & Decision Support and Patterns and Anomalies
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: D | Question #5 Correct Answer: B |
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