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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data for AI | - Data identification and governance
| |
| Topic 2: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 3: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 4: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 5: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 6: AI Model Development and Iteration | - Model building and validation
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
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 retraining
- B. Model Operationalization
- C. Model Drift
- D. Model reevaluation
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Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model. In which phase of CPMAI is this done?
- A. Phase V
- B. Phase I
- C. Phase IV
- D. Phase VI
- E. Phase III
- F. Phase II
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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. Identify the end users and their interactions.
- B. Analyze the solution's compliance requirements.
- C. Evaluate the system's scalability options.
- D. Consider the cost of implementation.
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A healthcare provider had physicians review a potential diagnostic AI application. During their final review, the project team along with the physicians, discovered that the AI model exhibits a higher than acceptable false-positive rate. Before making the go/no-go AI decision, which next step should be performed by the team?
- A. Increase the training data volume.
- B. Focus on the model's ethical implications.
- C. Reevaluate the business objectives and outcomes.
- D. Adjust the hyperparameters for better generalization.
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A project team is working on an AI initiative that requires ensuring the explainability and transparency of their selected algorithms. They have identified the business requirements and stakeholders. What is an effective way to address the project objectives?
- A. Leveraging generative AI for advanced data analysis
- B. Adopting a principle of least privilege for data access
- C. Using a rule-based approach to maintain simplicity
- D. Implementing a chain-of-thought prompting technique
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