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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Topic 2: Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks |
| Topic 3: Operationalize AI Solution | 17% | - Deploy AI systems into production - Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes |
| Topic 4: 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 |
| Topic 5: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A project team is preparing to move to the next phase of implementation with an AI system.
However, they are unsure about the system's transparency requirements. Which next step should the project manager prioritize?
A) Developing a detailed transparency documentation protocol
B) Creating a user-friendly interface for transparency reporting
C) Reviewing existing transparency standards and guidelines
D) Holding a stakeholder meeting to discuss transparency needs
2. At the start of an AI initiative, stakeholders are unclear about desired outcomes and success criteria. The team risks building a technically sound model that fails to deliver business value.
Which lifecycle phase should address this issue?
A) Problem framing
B) Model optimization
C) Monitoring
D) Deployment
3. During the configuration management of an AI/machine learning (ML) model, the team has observed inconsistent performance metrics across different test datasets. What will cause the inconsistency issue?
A) Insufficient model complexity
B) Incorrect data preprocessing steps
C) Overfitting the training data
D) Low variance in the test results
4. An inexperienced team is training a neural network model on a desktop computer which is taking a significant amount of time. Which recommended approach will increase the speed of the model training time?
A) Divide the dataset into multiple smaller datasets and train the model on each dataset over a desktop computer.
B) Train the model over multiple desktop computers.
C) Utilize a contractor to provide the model training portion.
D) Train the model on graphics processing units (GPUs).
5. As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
A) Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
B) Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
C) Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
D) Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |


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