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PMI CPMAI Exam Syllabus Topics:

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

1. An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk.
Which set of metrics is most appropriate?

A) Lines of code written
B) User satisfaction, containment rate, escalation accuracy, and privacy/compliance incidents
C) Number of features delivered
D) Response time only

2. A company is using an AI model to screen job applications. Over time, the project team notices that the model disproportionately rejects applications from certain educational institutions. What is the first step the project manager should take?

A) Analyze the historical data for inherent biases against these institutions.
B) Adjust the model's decision threshold for applications from these institutions.
C) Retrain the model with equal representation from all institutions.
D) Incorporate a fairness constraint into the model's objective function.

3. 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 company 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 team has and use the relevant data

4. A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs. They started with broad questions and then narrowed down the specific elements.
If the team had provided insufficient context, what would be the result?

A) The responses would lack relevance.
B) The output would include higher accuracy.
C) The model would perform more efficiently.
D) The model would generate more creative outputs.

5. Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?

A) Automatic hyperparameter tuning
B) Iterative modeling and evaluation
C) Automatic algorithm selection
D) Automatic model selection
E) Automatic model assessment

Solutions: