Sustaining AI Transformation and Continuous Improvement CAIPM Practice Questions (Page 5)

Questions 21–25

1. 21expert · hard

   A global retailer has a successful AI inventory-management system in its North American operations. The system has reduced stockouts by 25%. The company wants to scale the system to its European operations, but the European data is subject to stricter data protection regulations. The infrastructure team wants to use the same cloud deployment as North America. What is the most important consideration for the program manager?

   • AReplicate the North American deployment exactly to save time and cost
   • BFocus on the expected ROI of the European deployment before addressing compliance
   • CUse the same data pipeline but store data only in North America
   • DEnsure that the European deployment complies with local data residency and privacy regulations
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2. 22application · medium

   A bank successfully deployed an AI fraud-detection model in its credit card division. The model reduced fraud losses by 15%. The bank now wants to scale the model to its loan and mortgage divisions. What is the most important consideration before scaling?

   • AFocus only on infrastructure capacity to handle increased volume
   • BLimit scaling to the credit card division because it already works
   • CEnsure the model's training data is representative of the new divisions' transaction patterns
   • DDeploy the same model to all divisions without changes to save time
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4. 23application · medium

   A retail chain introduced an AI inventory-management system. Early adopters in the pilot stores love it, but the majority of store managers are not using it consistently. The program manager wants to increase adoption across the organization. Which strategy is most likely to build sustained momentum?

   • AIdentify and empower store managers who are enthusiastic about the system to act as peer champions.
   • BMake use of the system mandatory and monitor compliance through daily reports.
   • CProvide a one-time training session for all store managers.
   • DSend a company-wide email from the CEO explaining the benefits of the system.
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5. 24application · medium

   A logistics company has a successful AI route-optimization pilot in one region. The CTO wants to scale it to all regions. The pilot used a dedicated data science team and custom infrastructure. The company has limited data science resources and a tight budget. What is the most effective scaling strategy?

   • ADeploy the same model to all regions without adaptation, assuming the pilot conditions apply everywhere.
   • BHire a separate data science team for each region to ensure local optimization.
   • CCreate a center of excellence that documents best practices and provides reusable templates for other regions.
   • DOutsource the entire AI program to an external vendor to reduce internal effort.
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6. 25application · medium

   A bank's AI chatbot has reduced customer service call volume by 20%, but the team is unsure whether the investment is paying off. The CFO asks for a long-term value assessment that includes both cost savings and customer satisfaction. What should the program manager include in the assessment?

   • AA comparison of chatbot response accuracy against human agents, without financial data.
   • BA cost-benefit analysis that includes quantitative metrics like call reduction and qualitative metrics like customer satisfaction scores.
   • CA report that only lists the total development cost of the chatbot.
   • DA projection of future cost savings based on the current call reduction, ignoring customer satisfaction.
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