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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify Business Needs and Solutions26%- Problem framing and business alignment
  • 1. Define AI business objectives
    • 2. Feasibility and ROI analysis
      Topic 2: Data Preparation for AI- Data cleaning and transformation
      • 1. Data quality assurance
        • 2. Feature engineering and preparation
          Topic 3: AI Operationalization and Governance- Deployment and lifecycle management
          • 1. AI governance and responsible AI
            • 2. Continuous improvement and monitoring
              Topic 4: AI System Testing and Evaluation- Model evaluation and monitoring
              • 1. Performance evaluation and drift detection
                • 2. Explainability and reliability assessment
                  Topic 5: AI Model Development and Iteration- Model building and validation
                  • 1. Iterative delivery approach
                    • 2. Machine learning / generative AI model development
                      Topic 6: Data for AI- Data identification and governance
                      • 1. Data compliance and ethics
                        • 2. Data sourcing and selection

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                          PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q310-Q315):

                          NEW QUESTION # 310
                          Your team is tasked with selecting an algorithm for a supervised learning classification project.
                          Which algorithm might you choose?

                          Answer: A

                          Explanation:
                          K-nearest neighbor is a supervised learning algorithm used for classification tasks, making it suitable for supervised classification projects.


                          NEW QUESTION # 311
                          An aerospace company is integrating AI into their manufacturing process to enhance safety and efficiency. The project team needs to evaluate potential security threats to prevent unauthorized access to sensitive data. What is the highest risk?

                          Answer: C

                          Explanation:
                          Operationalizing a decentralized data storage system creates the highest security risk because sensitive manufacturing and AI data may be spread across multiple locations, increasing the attack surface and making access control, monitoring, governance, and protection harder to enforce consistently.


                          NEW QUESTION # 312
                          Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system.
                          What area of Trustworthy AI is being addressed here?

                          Answer: B


                          NEW QUESTION # 313
                          Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device", specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet. What's the most important thing to consider here?

                          Answer: C

                          Explanation:
                          For an edge device like a mobile phone used in remote locations with limited internet, the model must live on the device itself to ensure availability and functionality without relying on an internet connection.


                          NEW QUESTION # 314
                          A manufacturing firm plans to use AI to predict equipment failures. The team can access sensor data but it contains many missing values and out-of-range readings. What should the project manager prioritize first?

                          Answer: C

                          Explanation:
                          PMI-CPMAI stresses that AI delivery is data-driven and iterative, and that teams must manage the Data Understanding work to identify appropriate datasets and validate quality before model development. Missing values and out-of-range readings can materially distort training and inference, so the PMI-aligned priority is to characterize the data: understand sources, sampling frequency, sensor health, definitions, and the nature of missingness (random vs. systematic), then define cleansing/imputation and anomaly-handling strategies as part of data preparation.


                          NEW QUESTION # 315
                          ......

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