PMI CPMAI real dump : Cognitive Project Management in AI (PMI-CPMAI)

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PMI CPMAI Exam Syllabus Topics:

PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:

1. An AI solution performs well in testing but fails after deployment due to differences between training data and real-world production data. The team identifies discrepancies in feature distributions across environments. What is the primary issue affecting performance?

A) Hardware constraints
B) Data drift between environments
C) Model bias
D) Underfitting

2. Your team is tasked with selecting an algorithm for a supervised learning classification project.
Which algorithm might you choose?

A) Gaussian mixture
B) K-nearest neighbor
C) K-means
D) Q learning

3. A project manager is evaluating whether an AI model is ready for operationalization. The project team has ensured the model meets accuracy and performance requirements and has conducted extensive testing. However, uncertainty about the model's operationalization in different environments and its compliance with regulatory standards still exists. What should the team do next?

A) Reassess the project's data preparation phase.
B) Evaluate the model's adaptability to diverse conditions.
C) Implement additional noncognitive solutions.
D) Consider retraining the model for better performance.

4. A project manager is overseeing the development of an AI system and is concerned about maintaining user privacy. The project involves collecting sensitive user data with potential legal and compliance issues to consider.
What should the project manager prioritize to help ensure the AI system adheres to privacy requirements?

A) Implementing a minimalistic data collection policy
B) Developing a comprehensive data anonymization strategy
C) Establishing robust data encryption protocol for privacy
D) Ensuring regular impact assessments are conducted to meet privacy laws

5. A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?

A) Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
B) Hire an entirely new team making sure there is at least one data scientist on this centrteam
C) Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
D) Bring in data centric methodology best practices to get this project back on track

Solutions: