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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Data for AI- Data identification and governance
  • 1. Data compliance and ethics
    • 2. Data sourcing and selection
      AI Operationalization and Governance- Deployment and lifecycle management
      • 1. AI governance and responsible AI
        • 2. Continuous improvement and monitoring
          AI System Testing and Evaluation- Model evaluation and monitoring
          • 1. Performance evaluation and drift detection
            • 2. Explainability and reliability assessment
              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 Model Development and Iteration- Model building and validation
                      • 1. Machine learning / generative AI model development
                        • 2. Iterative delivery approach

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q47-Q52):

                          NEW QUESTION # 47
                          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: A

                          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 # 48
                          You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

                          Answer: A,B,C,D


                          NEW QUESTION # 49
                          A project team is assessing a customer dataset and finds that 22 percent of records have a null value in the primary address field, and several records list contradictory account open dates.
                          Which two data quality dimensions are most directly affected?

                          Answer: C

                          Explanation:
                          Missing values in a required field represent a completeness problem, while contradictory values for the same attribute represent a consistency problem. Identifying which specific dimensions are affected determines the appropriate remediation approach during Data Preparation.


                          NEW QUESTION # 50
                          The team is working to build a data preparation pipeline for the conversational chatbot project.
                          Which phase of CPMAI is this done?

                          Answer: C

                          Explanation:
                          Phase II of CPMAI focuses on data preparation, including building pipelines for data collection, cleaning, and labeling needed for model training.


                          NEW QUESTION # 51
                          A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months.
                          What's wrong with this timeframe?

                          Answer: A


                          NEW QUESTION # 52
                          ......

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