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PMI CPMAI Exam Syllabus Topics:

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

Question 1

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

Question 2

A project team is preparing to move to the next phase of their AI project. The team needs to ensure that all transparency and explainability requirements are met. Which activity should the project team perform?

A. Document the decision-making process of the AI model.
B. Establish a feedback mechanism for ongoing evaluation.
C. Conduct a thorough data quality assessment.
D. Define the ethical guidelines for the AI project.

Question 3

A healthcare organization is developing an Al model to predict patient outcomes. The project manager needs to address issues of missing or incorrect data before training the model. What is an effective method to help ensure data quality?

A. Performing systematic data validation
B. Enhancing with data augmentation techniques
C. Implementing an AI data needs assessment
D. Applying predictive data algorithms

Question 4

During the planning phase, a project manager identifies multiple patterns for their AI project.
Which two actions should the project manager do? (Choose two.)

A. Select patterns based on stakeholder preference.
B. Combine all patterns into a single comprehensive iteration.
C. Split the project into iterations for different patterns.
D. Proceed with the most complex pattern to challenge the team.
E. Focus on noncognitive parts before addressing cognitive patterns.

Question 5

Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?

A. Data privacy issues leading to data sharing concerns
B. Nothing is wrong, algorithmic decisions will never be 100%
C. Biased data sets leading to algorithmic discrimination
D. Generative AI models hallucinating data results

Solutions: