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PMI CPMAI Exam Syllabus Topics:

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

Question 1

Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project. What is the best course of action to take with this data?

A. Data selection and attribute pruning to reduce overall size and data complexity.
B. Careful algorithm selection that reduces the need for data.
C. Data Deduping to reduce overall size and data complexity.
D. Data integration focused on reducing the number of data sources.

Question 2

Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, your team doesn't have access to all the data it needs. What's the best course of action?

A. Move ahead as planned so you stay on time with your project.
B. Do not move forward until you have access to all the data you need.
C. Cautiously move forward knowing you may need to pause mid-project which is ok.
D. Move ahead as planned and hope you get access to the data once you need it. Since you're using an iterative approach you can always go back to steps as needed later on.

Question 3

A model has passed evaluation and is approved for production. The project team has no defined process for versioning models, tracking which data version produced them, or rolling back a deployment.
What does this project lack?

A. MLOps practices for model lifecycle management
B. An explainability assessment
C. A data governance framework
D. A change control board

Question 4

When building your model, you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective. At what phase of CPMAI is this most appropriate to do this?

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

Question 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. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
B. Hire an entirely new team making sure there is at least one data scientist on this centrteam
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

Solutions: