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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 13% | - Deep Learning and Neural Networks - Algorithms and models (e.g., NLP, Computer Vision) - Supervised, Unsupervised, and Reinforcement Learning |
| Managing AI | 8% | - Stakeholder management - Risk management in AI projects - Managing AI project teams and resources |
| CPMAI Methodology | 41% | - Phase I: Problem Identification
|
| AI Fundamentals | 16% | - Types of AI and Machine Learning - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence |
| Trustworthy AI | 9% | - Ethical considerations and bias - Transparency and explainability - Privacy and security |
| Data for AI | 13% | - Data strategy and governance - DataOps concepts - Data preparation and preprocessing |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question 1
The project team at an IT services company is working on an AI-based customer support chatbot.
To help ensure the chatbot functions effectively, they need to define the required data. Which method meets the project requirements?
A. Gathering historical customer interaction logs for training data
B. Using synthetic data generated from sample customer conversations
C. Integrating feedback from beta customers to refine the model
D. Developing a new script based on anticipated customer queries
Question 2
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?
A. Conducting a stakeholder impact analysis
B. Performing continuous monitoring and auditing
C. Using an explainability framework
D. Implementing a data encryption protocol
Question 3
An aerospace company is planning to integrate AI for predictive maintenance. The project team needs to identify which parts of the project require cognitive AI solutions. Which method should the project manager perform to meet the objective?
A. Implementing a rule-based system to classify AI project elements based on predefined criteria
B. Conducting a series of workshops with domain experts to map out AI dependencies
C. Employing a multicriteria decision analysis tool to categorize AI project components
D. Utilizing an AI-driven workflow engine to automate project segmentation
Question 4
An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?
A. Understand the data characteristics.
B. Evaluate the data freshness and relevance.
C. Delete the suspicious data manually.
D. Create a data visualization.
Question 5
A project manager is evaluating an AI model's performance to determine its business impact. The model has shown high accuracy during testing but has not been implemented yet in a live environment. The project manager needs to decide what to assess to ensure the model's effectiveness. What should the project manager do?
A. Determine the scalability for future expansions.
B. Conduct a cost-benefit analysis of the model's implementation.
C. Evaluate the ethical implications of operationalizing the model.
D. Check if the training data represents real-world scenarios.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: D |
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