Free PDF Quiz 2026 PMI CPMAI: Pass-Sure Reliable Cognitive Project Management in AI (PMI-CPMAI) Test Cram

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Identify Business Needs and Solutions26%- Problem framing and business alignment
  • 1. Feasibility and ROI analysis
    • 2. Define AI business objectives
      Data Preparation for AI- Data cleaning and transformation
      • 1. Feature engineering and preparation
        • 2. Data quality assurance
          AI Operationalization and Governance- Deployment and lifecycle management
          • 1. Continuous improvement and monitoring
            • 2. AI governance and responsible AI
              AI Model Development and Iteration- Model building and validation
              • 1. Iterative delivery approach
                • 2. Machine learning / generative AI model development
                  Data for AI- Data identification and governance
                  • 1. Data sourcing and selection
                    • 2. Data compliance and ethics
                      AI System Testing and Evaluation- Model evaluation and monitoring
                      • 1. Explainability and reliability assessment
                        • 2. Performance evaluation and drift detection

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                          CPMAI Valid Test Question - CPMAI Exam Sample

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q295-Q300):

                          NEW QUESTION # 295
                          Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?

                          Answer: B

                          Explanation:
                          Responsible AI focuses on ensuring AI systems are developed and used ethically, with proper guidelines and safeguards to promote positive and fair outcomes.


                          NEW QUESTION # 296
                          A healthcare AI model achieves high predictive accuracy but lacks transparency in decision- making. Regulators require clear explanations for each prediction to ensure patient safety and trust. What is the primary risk of deploying this model as-is?

                          Answer: D

                          Explanation:
                          Lack of explainability may violate healthcare regulations and safety standards.


                          NEW QUESTION # 297
                          Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)

                          Answer: B,C,E,H


                          NEW QUESTION # 298
                          A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model's predictions are biased toward a particular class.
                          What is an effective technique to mitigate this bias?

                          Answer: A

                          Explanation:
                          Stratified sampling preserves appropriate class proportions when creating training or evaluation subsets. This helps ensure that each class is adequately represented, reducing class-related sampling bias and producing a more balanced assessment of the churn model.


                          NEW QUESTION # 299
                          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?

                          Answer: C

                          Explanation:
                          Performing systematic data validation helps identify and correct missing, inaccurate, inconsistent, or incomplete patient data before model training, improving the reliability of the data used to predict patient outcomes.


                          NEW QUESTION # 300
                          ......

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