Integrating AI into Enterprise Systems CAIPM Practice Questions (Page 8)

Questions 36–40

1. 36application · medium

   A retail chain is integrating an AI model to forecast inventory demand across its stores. The model needs data from the point-of-sale (POS) system, the supply chain management system, and external weather data. The POS data is stored in different formats across stores, and some stores have missing sales records. What should the data integration strategy include?

   • AIntegrate only the supply chain data because it is more reliable than POS data.
   • BUse the POS data as-is and let the AI model handle missing values automatically.
   • CCreate a centralized data lake that ingests all data sources, standardizes formats, and applies data quality rules to handle missing values.
   • DStore each store's data separately and train a separate model for each store to avoid format issues.
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2. 37application · medium

   An e-commerce company is integrating a recommendation engine into its website. The AI model is served via a REST API that the web front-end calls. The company wants to validate that the integration works correctly under peak holiday traffic. What should the testing strategy include?

   • ATest the API with a single user request to confirm it returns a recommendation.
   • BPerform load testing against the API with simulated peak traffic to verify response times and error rates.
   • CRun unit tests on the model's code only, without testing the API integration.
   • DDeploy the integration to production and monitor for issues during the holiday season.
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4. 38application · medium

   An insurance company is integrating an AI model to automate claims processing. The model requires data from the claims management system, customer relationship management (CRM) system, and external weather data for certain claims. The data from the claims system is updated in real time, but the CRM data is only refreshed nightly. What should the data integration strategy include?

   • AIngest all data in nightly batches to ensure consistency.
   • BForce the CRM system to provide real-time data by changing its architecture.
   • CUse only the claims data and ignore CRM data to simplify the integration.
   • DUse a data integration platform that supports both real-time streaming for claims data and batch ingestion for CRM data.
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5. 39expert · hard

   A global bank is integrating an AI system that screens cross-border transactions for money laundering. The bank operates in multiple jurisdictions, each with different data protection laws. Some jurisdictions prohibit personal data from leaving their borders. The bank wants to use a single AI model centrally. What should the program manager do?

   • AUse a federated learning approach where the model is trained locally and only model updates are shared.
   • BDeploy the AI model in each jurisdiction where data cannot leave, and use a central model for other regions.
   • CCentralize all data in the bank's headquarters and apply the same model globally.
   • DAnonymize all personal data before sending it to the central model.
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6. 40application · medium

   A logistics company has integrated an AI model into its route optimization system. The model runs on a nightly batch job to update delivery routes. The company wants to ensure the model continues to perform well as traffic patterns and delivery areas change. What should the monitoring and maintenance plan include?

   • ARetrain the model daily with all historical data, regardless of performance metrics.
   • BSet up alerts for system uptime only, and do not retrain the model unless it fails completely.
   • CFreeze the model after deployment to ensure consistent behavior and avoid unexpected changes.
   • DMonitor the model's prediction accuracy against actual outcomes and retrain the model periodically with new data.
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