A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?

• A.Increase model complexity
• B.Use cross-validation techniques
• C.Remove validation data
• D.Increase training epochs indefinitely

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A telecommunications company plans to operationalize a new AI-powered network optimization tool. The project manager needs to identify and map out all necessary tasks and associated resources. Which activity should the project manager perform?

• A.Applying an earned value management (EVM) methodology
• B.Developing a work breakdown structure (WBS)
• C.Conducting a feasibility study to assess the technical and financial viability
• D.Using a Gantt chart to schedule and monitor resource deployment timelines

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

• A.K-means
• B.Gaussian mixture
• C.K-nearest neighbor
• D.Q learning

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Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What's the best course of action with how to proceed?

• A.Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
• B.Do not move forward and cancel the project altogether.
• C.Move forward with the project as planned. The problem definition will become clear later on in the project.
• D.Do not move forward until you can determine a clear problem definition.

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A team is evaluating different AI models for their project. They are considering error rates and overall performance. If the team had selected a model based solely on the error rate, what would be the outcome?

• A.A balanced performance across all metrics
• B.An increase in stakeholder satisfaction based on performance
• C.A better performance across the chosen domains
• D.A potential to overlook other critical performance metrics

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