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

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PMI CPMAI Exam Syllabus Topics:

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

1. For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A) Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
B) Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
C) Understanding which algorithms are best for your data needs.
D) Collect enormous amounts of data - the more data the better.

2. Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned. What would likely have helped avoid this conflict?

A) Everyone on the team should understand the differences between automation and autonomous systems.
B) Nothing - running automation projects like autonomous projects is the correct thing to do.
C) Senior management should become involved in the project.
D) Applying a hybrid approach of automation and AI best practices would have achieved better results.

3. A financial services organization deploys an AI credit-scoring model. Post-deployment monitoring reveals systematic underprediction for a protected demographic group, potentially affecting loan approvals. Stakeholders are concerned about fairness and compliance risks. What should the project manager prioritize FIRST?

A) Pause the system indefinitely
B) Replace the algorithm entirely
C) Conduct a bias and fairness audit
D) Retrain the model with additional data

4. A project manager is preparing a final report on an AI project. The report must highlight lessons learned, focusing on ethical concerns and compliance with data regulations. In addition, the team has identified multiple ethical issues related to data privacy during the project. What is an effective approach to address the situation for future AI projects?

A) Provide additional training on ethical AI practices.
B) Develop a transparent data compliance usage policy.
C) Implement a robust ethical data governance framework.
D) Increase the frequency of compliance audits.

5. You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A) Use noise reduction techniques to reduce all forms of data noise
B) If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C) Store the data in a data warehouse
D) Implement a new data privacy policy

Solutions: