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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Model Development and Iteration | - Model building and validation
| |
| Topic 2: Data for AI | - Data identification and governance
| |
| Topic 3: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 4: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 5: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 6: AI Operationalization and Governance | - Deployment and lifecycle management
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A manufacturing company is considering implementing an AI solution to optimize its supply chain.
The project manager needs to determine if AI is necessary for this task. Which action will address the requirements?
A) Identifying noncognitive versus AI methods used in supply chain management
B) Evaluating the scalability of AI solutions for supply chain optimization
C) Assessing the cost-benefit ratio of an AI implementation for the supply chain
D) Determining the specific cognitive tasks that Al can perform within the supply chain
2. A financial services organization deploys an AI credit-scoring model. Post-deployment monitoring reveals systematic underprediction for a protected demographic group, potentially affecting loan approvals. Stakeholders are concerned about fairness and compliance risks. What should the project manager prioritize FIRST?
A) Retrain the model with additional data
B) Replace the algorithm entirely
C) Conduct a bias and fairness audit
D) Pause the system indefinitely
3. 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) Adjust the hyperparameters for better generalization.
D) Reevaluate the business objectives and outcomes.
4. A healthcare AI model achieves high predictive accuracy but lacks transparency in decision- making. Regulators require clear explanations for each prediction to ensure patient safety and trust. What is the primary risk of deploying this model as-is?
A) Reduced system speed
B) Increased operational costs
C) Data duplication issues
D) Regulatory non-compliance
5. You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?
A) Hyperpersonalization
B) K-means
C) K Nearest Neighbor
D) Neural Network
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |







