PMI CPMAI

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

1. Your team is testing the NLP model they just created to make sure it's performing as expected.
Some of your team members want to move this model to production and move to the next iteration.
What's wrong with this workflow?

A) You need to make sure the AI Go/No Go questions have been addressed
B) Team members should not be able to move to new projects until senior management signs off
C) Nothing is wrong with this workflow. You can move to the next iteration
D) Model Evaluation requires continuous model evaluation, retraining, and operationalization

2. You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What's the best way to approach this?

A) Take inventory of all data your team has and use the relevant data
B) Take all the data your company has as well as purchase additional external data
C) Take all the existing data you have and apply it to this project
D) Take inventory of all data your company has and use the relevant data

3. Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What's the best course of action with how to proceed?

A) Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
B) Do not move forward until you can determine a clear problem definition.
C) Do not move forward and cancel the project altogether.
D) Move forward with the project as planned. The problem definition will become clear later on in the project.

4. You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with.
Which algorithm is the best choice given these constraints?

A) Ensemble models
B) Generative AI
C) Naive Bayes
D) Neural Networks

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

Solutions: