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PMI CPMAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Topic 2: Data for AI | - Data identification and governance
| |
| Topic 3: AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| 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: Data Preparation for AI | - Data cleaning and transformation
|
PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:
An aerospace firm is developing an AI system for predictive maintenance of their aircraft. The project team needs to define the required data to train the model. Which activity should the project manager implement?
- A. Developing a comprehensive data collection strategy
- B. Setting up real-time data streaming from aircraft sensors
- C. Conducting a pilot test with a small dataset
- D. Implementing data cleaning and preprocessing routines
Correct Answer: A 🗳️
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During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?
- A. Model Selection
- B. Algorithm Selection
- C. Model tuning
- D. Model training
Correct Answer: A 🗳️
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Your team is working on an AI enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data, you realize you don't have enough data to train the model. What's the best course of action?
- A. Ask your customer service team to generate additional data for you to use for the project
- B. Use the data that you have and keep the project moving
- C. Do not move forward with the project
- D. Research what Third-Party Models are available and purchase them to keep the project moving
Correct Answer: D 🗳️
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Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model. What critical error did your team make?
- A. They did not have a model retraining pipeline that took into account models
- B. They did not practice model versioning and keep all versions of the model
- C. They did not have data governance in place
- D. They did not practice model iteration and properly iterate on the model
Correct Answer: B 🗳️
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As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?
- A. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're like to expand to in the coming year
- B. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
- C. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
- D. You're too small of an organization to be worried about laws at the moment
Correct Answer: A 🗳️
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