Sustaining AI Transformation and Continuous Improvement CAIPM Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A financial services firm deployed an AI chatbot to handle customer inquiries. After six months, the chatbot handles 40% of inquiries, but the firm is unsure whether the investment is paying off. The customer service team reports that average handling time has decreased, but customer satisfaction scores have slightly declined. What is the most appropriate next step for the program manager?

   • AExpand the chatbot to more channels immediately to increase adoption
   • BFocus only on the reduction in average handling time as the key success metric
   • CDiscontinue the chatbot because customer satisfaction declined
   • DConduct a comprehensive ROI analysis that includes cost savings, customer satisfaction, and deflection rate
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2. 2foundation · easy

   What is the primary purpose of monitoring data drift in an AI system?

   • ATo measure the business impact of the AI system
   • BTo detect when the statistical properties of input data change, which may degrade model performance
   • CTo ensure the model is retrained daily regardless of need
   • DTo track the number of users interacting with the system
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4. 3expert · hard

   A manufacturing company has an AI quality-inspection system that has reduced defects by 30%. The system requires significant computational resources, and the IT budget is limited. The company is considering whether to invest in additional GPU capacity to improve the system's speed or to use the budget for other AI initiatives. The system's current speed is adequate for production. What should the program manager do?

   • AEvaluate the ROI of the GPU investment against the potential ROI of other AI initiatives
   • BReduce the system's accuracy to lower computational costs
   • CContinue with the current system and defer all other AI initiatives
   • DInvest in GPU capacity because the system is already delivering value
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5. 4application · medium

   A logistics company uses an AI model to optimize delivery routes. The model's performance is monitored weekly, and the team meets monthly to review metrics. Recently, the model's performance has been declining, but the team only notices after a month. What should the team implement to detect and respond to performance issues more quickly?

   • AIncrease the frequency of performance monitoring to daily and set alert thresholds
   • BWait for the next monthly review to discuss the decline
   • CReplace the model with a simpler rule-based system
   • DRetrain the model automatically every week without monitoring
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   What is the primary purpose of a model retirement process?

   • ATo automatically retrain models without human intervention
   • BTo decommission models that are no longer effective or relevant in a controlled manner
   • CTo keep all models running indefinitely for historical reference
   • DTo archive models without any documentation
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