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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 2: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 3: Data for AI | - Data identification and governance
| |
| Topic 4: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 5: AI Model Development and Iteration | - Model building and validation
| |
| Topic 6: AI Operationalization and Governance | - Deployment and lifecycle management
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
Question #1
A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis.
Which action best addresses the problem during data preparation?
A. Skip validation to save time
B. Replace real data with only synthetic data
C. Determine and apply data transformation and standardization steps
D. Ignore the inconsistency because the model will learn patterns anyway
Question #2
A company plans to operationalize an AI solution. The project manager needs to ensure model performance is meeting selected thresholds before release. What is an effective way to confirm these thresholds before this release?
A. Testing against validation datasets
B. Conducting a series of penetration tests
C. Running multiple end-user acceptance tests
D. Implementing an impact evaluation
Question #3
A project manager is considering different project management approaches for an AI solution deployment. They need to ensure the approach allows for iterative improvements and accommodates changing requirements. Which approach is effective in this situation?
A. Hybrid
B. Incremental
C. Adaptive/agile
D. Predictive
Question #4
Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing illnesses. The AI system is integrated with several medical databases. This allowed the AI system to learn from new patient data and adapt to the latest medical knowledge and practices. The final project report indicated that the AI model had degraded over time which impacted reliability and effectiveness. The AI system must comply with healthcare regulations from various countries.
What is the likely cause for the degradation issue?
A. Data drift affecting model precision
B. Changes in business model requirements
C. Impact of data drift on model accuracy
D. Inadequate initial model validation
Question #5
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. Implementing a data encryption protocol
B. Performing continuous monitoring and auditing
C. Conducting a stakeholder impact analysis
D. Using an explainability framework
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: A | Question #3 Correct Answer: C | Question #4 Correct Answer: C | Question #5 Correct Answer: B |
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