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

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

2. You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

A) Explainable AI
B) Governed AI
C) Ethical AI
D) Transparent AI
E) Responsible AI

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

4. You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project - AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?

A) Determine if the project fits within the scope, budget, and timeline set out.
B) Determine whether the project has a cognitive technology component and meets a short-term need.
C) Determine the long-term need for the organization and build the project to that long-term goal.
D) Evaluate whether the solution can be done with automation.

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

Solutions: