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

1. You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

A) Understanding of algorithms
B) Strong math skills, especially in calculus and statistics
C) Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
D) Automation skills, especially around creating RPA bots
E) Prompt engineering skills
F) Critical thinking skills

2. You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?

A) Reinforcement Learning
B) Generative AI
C) Pre-Trained Models
D) Transfer Learning

3. Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

A) Descriptive Analytics
B) Projective Analytics
C) Predictive Analytics
D) Diagnostic Analytics

4. Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?

A) Data Scientists
B) Senior management
C) Data Engineering and Data Scientists
D) All roles are critical to staff in the Four different AI Tech environments
E) Data Engineering

5. During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A) Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.
B) Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
C) Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
D) Evaluate your data and use only what you have and build all models in house.

Solutions: