PMI PMI-CPMAI Exam Study Material
PMI Certified Professional in Managing AI- 144 Questions & Answers
- Update Date : June 20, 2026
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Question 1
An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?
A. Verify data quality and stakeholder alignmentB. Proceed with development despite data issues
C. Focus solely on technology upgrades, not data
D. Launch the AI project without further assessment
Question 2
A government agency is adopting an AI/machine learning (ML) model to analyze large sets of public data for policy making. It is crucial that the project team ensures the accuracy of the model's predictions. If the project team needs to validate the model, which action should they perform?
A. Ensure adherence to coding standards.B. Conduct a single comprehensive validation.
C. Utilize a diverse set of test cases.
D. Implement continuous integration testing.
Question 3
A financial institution is planning to use AI capabilities to detect fraudulent transactions. The project manager needs to ensure that all necessary requirements are met before proceeding. What is a necessary initial task?
A. Evaluating the accuracy of current fraud detection methodsB. Determining the scalability of AI solutions for transaction monitoring
C. Identifying the primary stakeholders and their needs
D. Assessing the ethical implications of using AI for fraud detection
Question 4
To determine if an AI solution is appropriate for an upcoming project, the project manager needs to evaluate whether the project requires a cognitive approach. What should the project manager address?
A. Existing well-defined business objectivesB. Estimated project cost
C. Required level of interpretability
D. Potential non-cognitive alternatives
Question 5
A fintech AI project uses third-party data sources for credit risk modeling. The project manager is concerned about compliance and accountability if the external data quality changes. Which control best supports responsible and trustworthy AI delivery?
A. Establish data governance and supplier controls, including auditability and monitoringB. Remove all external data sources immediately
C. Only document model performance once at launch
D. Allow each team to apply its own data definitions