Integrating AI into Enterprise Systems CAIPM Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   Which AI deployment model is most appropriate when an enterprise must process data at the source to minimize latency and operate without continuous internet connectivity?

   • AEdge deployment
   • BOn-premises deployment
   • CHybrid deployment
   • DPublic cloud deployment
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2. 27expert · hard

   A hospital has deployed an AI diagnostic support system that runs on-premises. The system must be available 99.9% of the time. The IT team has limited staff. The system's model is updated monthly. What maintenance strategy best balances availability and staff workload?

   • ASchedule weekly maintenance windows to apply updates and check system health.
   • BImplement automated health checks and rolling updates for model changes.
   • CDeploy the system to the cloud to reduce maintenance burden.
   • DAssign a full-time engineer to manually monitor and update the system.
   Reveal Solution Discussion Select an answer first
4. 28expert · hard

   A manufacturing company is integrating an AI quality-inspection system with its legacy manufacturing execution system (MES), which runs on an old server and uses a proprietary database. The AI system needs real-time production data. The MES vendor is out of business. What is the most reliable integration approach?

   • AHave operators manually enter production data into the AI system.
   • BUse a database replication tool to copy the MES database to a modern database that the AI system can query.
   • CReplace the MES with a modern system that has built-in APIs.
   • DReverse-engineer the MES's proprietary API and build a custom connector.
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5. 29foundation · easy

   In designing the technical architecture for embedding AI into an enterprise system, which component is typically responsible for exposing model predictions to other applications?

   • AData lake
   • BAPI gateway
   • CETL pipeline
   • DModel training script
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6. 30application · medium

   A multinational bank is integrating an AI system for credit scoring. The system will be deployed in multiple countries, each with different data protection regulations. The bank wants to ensure compliance while maintaining a consistent customer experience. What should the AI program manager do?

   • AStore all customer data in the bank's headquarters country and process it there, regardless of local laws.
   • BUse a governance framework that maps data protection requirements for each country and configures the AI system to comply with local regulations.
   • CDeploy the AI system in a single cloud region and apply the strictest data protection rules to all customers globally.
   • DAvoid deploying the AI system in countries with strict data protection laws to reduce compliance burden.
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