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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance - Define data requirements and sources |
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation |
| Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?
A) Careful algorithm selection that reduces the need for data.
B) Data integration focused on reducing the number of data sources.
C) Data selection and attribute pruning to reduce overall size and data complexity.
D) Data Deduping to reduce overall size and data complexity.
2. A project manager is tasked with overseeing the implementation of an AI model for financial forecasting. They need to ensure the model's predictions are reliable. If the model's error rate exceeds acceptable boundaries, what will occur next?
A) Higher than expected computational costs
B) Increased stakeholder confidence that the project team will correct
C) Operationalization delays due to model retraining
D) Reduced need for human oversight since additional AI models will be used
3. An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?
A) Document the performance metrics for the model.
B) Ensure go/no-go questions have well-defined answers.
C) Conduct a final assessment of the data quality.
D) Prepare a report on the model's scalability.
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) Assessing scalability requirements
B) Determining automation potential
C) Evaluating cognitive alternatives
D) Identifying ethical concerns
5. Your team is working on an AI enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data, you realize you don't have enough data to train the model. What's the best course of action?
A) Use the data that you have and keep the project moving
B) Ask your customer service team to generate additional data for you to use for the project
C) Research what Third-Party Models are available and purchase them to keep the project moving
D) Do not move forward with the project
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: C |



