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

1. You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?

A) Phase VI
B) Phase I
C) Phase IV
D) Phase V
E) Phase II
F) Phase III

2. 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 iteration and properly iterate on the model
C) They did not practice model versioning and keep all versions of the model
D) They did not have data governance in place

3. Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A) Nothing - running automation projects like autonomous projects is the correct thing to do.
B) Senior management should become involved in the project.
C) Everyone on the team should understand the differences between automation and autonomous systems.
D) Applying a hybrid approach of automation and AI best practices would have achieved better results.

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

5. Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

A) Real-time prediction
B) Stream learning
C) Web service / Microservice
D) Batch prediction

Solutions: