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

1. 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 practice model versioning and keep all versions of the model
B) They did not have data governance in place
C) They did not practice model iteration and properly iterate on the model
D) They did not have a model retraining pipeline that took into account models

2. 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) Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
C) Bring in data centric methodology best practices to get this project back on track
D) Hire an entirely new team making sure there is at least one data scientist on this new team

3. You are working for a large multinational organization and have been assigned to a new project.
For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address (Select all that apply.):

A) Securing data at rest
B) Securely storing all data collected for training purposes
C) Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
D) Securing model data and metadata

4. Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected.
What could have been done better to get the team to feel comfortable with this technology and use it? (Select all that apply.)

A) Have the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
B) Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.
C) Have upper management relay to employees this tool is to augment, and not replace their jobs.
D) Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.

5. In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

A) Find a source of already labeled data
B) Contract with Third Party Data Labeling Firms
C) Get your Users to Do it
D) Hire a Contractor Workforce

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