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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks |
| Topic 2: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 3: Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure - Define data requirements and sources |
| 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: Operationalize AI Solution | 17% | - Manage change, adoption, and governance post-launch - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes |
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question 1
A logistics company is planning to separate cognitive and noncognitive parts of an AI project.
They need to manage teams working on these parts. What is a necessary initial task?
A. Utilizing a project management software to monitor progress
B. Implementing a customer-driven feedback loop in planning
C. Developing a project charter outlining responsibilities for each team
D. Conducting a skills assessment to assign team members accordingly
Question 2
A fraud detection model is evaluated on a dataset in which 0.4 percent of transactions are fraudulent. The model reports 99.6 percent accuracy.
How should the project manager interpret this result?
A. The model requires additional training epochs to improve accuracy
B. The model meets the accuracy threshold and can be operationalized
C. Accuracy is misleading for this distribution; precision and recall must be assessed
D. The evaluation dataset is too small to support a conclusion
Question 3
An AI-powered chatbot occasionally generates inappropriate or harmful responses despite strong training performance. The organization wants to reduce risks while maintaining usability. What is the most effective mitigation strategy for this issue?
A. Implement guardrails and content filters
B. Accelerate deployment cycles
C. Remove monitoring mechanisms
D. Increase dataset size
Question 4
A telecommunications company is implementing an AI-driven customer support system. The project manager is responsible for overseeing the data evaluation. They need to ensure that the AI system provides accurate and helpful responses to customer queries. What is an effective method that helps to ensure these objectives are achieved?
A. Regularly updating the AI system's knowledge base with the latest information and feedback from customer interactions
B. Implementing a static rule-based system alongside the AI system to handle complex customer questions
C. Relying on periodic training sessions for customer support staff to improve their understanding of the AI system
D. Conducting quarterly performance reviews using customer satisfaction surveys
Question 5
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. Consider the cost of implementation.
B. Analyze the solution's compliance requirements.
C. Evaluate the system's scalability options.
D. Identify the end users and their interactions.
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: D |
