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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Topic 2: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| Topic 3: Data Preparation for AI | - Data cleaning and transformation
| |
| Topic 4: AI Model Development and Iteration | - Model building and validation
| |
| Topic 5: Data for AI | - Data identification and governance
| |
| Topic 6: AI Operationalization and Governance | - Deployment and lifecycle management
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
1. A consulting firm is determining the feasibility of an AI project. They need to justify the use of AI over noncognitive solutions. The project manager has listed potential noncognitive alternatives.
What is an effective method to support an AI approach?
A) Relying only on industry trends favoring AI adoption
B) Focusing on the novelty and technological AI appeal
C) Conducting a cost-benefit analysis comparing AI and noncognitive solutions
D) Emphasizing the simplicity and reliability of noncognitive solutions
2. Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Choose all that apply.)
A) The algorithm code
B) Supporting training data
C) Bias-variance tradeoff
D) Hyperparameter settings
3. A company is operationalizing an AI system for automated customer service. The system appears to favor queries in one language over others, despite having been trained on a multilingual dataset. Which two issues should the project lead investigate? (Choose two.)
A) Examine whether the preprocessing techniques varied across languages.
B) Evaluate the system's response times for different languages.
C) Check the distribution of training samples per language.
D) Determine whether the neural network architecture supports multilingual processing.
E) Presume the dataset is at fault, without conducting a detailed analysis.
4. A fraud detection system must minimize false positives to avoid incorrectly flagging legitimate transactions. The team is selecting evaluation metrics to optimize model performance accordingly. Which metric should be prioritized in this scenario?
A) Precision
B) F1-score
C) Recall
D) Accuracy
5. During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?
A) Model tuning
B) Algorithm Selection
C) Model Selection
D) Model training
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,B,D | Question # 3 Answer: A,C | Question # 4 Answer: A | Question # 5 Answer: C |
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