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

1. Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

A) Supervised Learning
B) All would work equally well
C) Unsupervised Learning
D) Reinforcement Learning

2. Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it.
What's going on here?

A) The bot is falling into The "Uncanny Valley"
B) Lack of understanding the robot's usefulness
C) Bias towards the robot
D) Safety and reliability issues that impact bot usefulness

3. Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?

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

4. 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

5. You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

A) Data Acquisition / Ingest / Capture
B) Retraining Pipelines
C) ELT Pipeline
D) Feature Engineering

Solutions: