AI Platforms, Tools, and Ecosystem Integration CAIPM Practice Questions (Page 5)

Questions 21–25

1. 21application · medium

   A logistics company wants to automate a monthly reporting workflow that extracts data from multiple sources, runs a forecasting model, and distributes results to stakeholders. They want to minimize manual intervention and ensure the workflow runs reliably. Which approach should they use?

   • AUse an orchestration tool to schedule the data extraction, model execution, and report distribution as a pipeline
   • BHave a data engineer manually run the model and email the results each month
   • CWrite a single script that performs all steps and run it on a developer's laptop
   • DUse a cron job to trigger the model execution but skip data validation
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2. 22expert · hard

   A government agency is integrating an AI platform for document classification. The agency handles sensitive citizen data and must comply with strict data sovereignty laws. The AI platform is cloud-based, and the agency's IT team is concerned about data leaving the country. The agency also needs to audit all data access and processing. What should the agency implement?

   • AUse the AI platform's public endpoints but encrypt all data in transit and rely on the vendor's compliance certifications
   • BDeploy the AI platform in a dedicated cloud region within the country, use private endpoints for data access, and enable detailed logging
   • CStore data on-premises and use the AI platform only for processing, without transferring data to the cloud
   • DUse a third-party middleware to anonymize data before sending it to the AI platform, but do not log any requests
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4. 23application · medium

   An e-commerce company has a machine learning model that experiences high latency during peak shopping hours. The model is deployed on a fixed-size cluster, and the company wants to improve performance without over-provisioning resources during off-peak times. What should the company do?

   • AOptimize the model by reducing its complexity, even if it slightly reduces accuracy
   • BMove the model to a less powerful but cheaper instance type
   • CImplement auto-scaling for the model deployment to adjust resources based on traffic demand
   • DIncrease the cluster size permanently to handle peak traffic at all times
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5. 24application · medium

   A healthcare startup needs to deploy a computer vision model for detecting anomalies in medical images. They have a small data science team with limited MLOps experience, require HIPAA-compliant data handling, and need to integrate with their existing electronic health record (EHR) system. Which platform choice best aligns with these needs?

   • AAn enterprise AI platform focused on predictive maintenance for industrial equipment
   • BA general-purpose cloud compute service with self-managed ML libraries
   • CA cloud-based AI platform with managed ML services and built-in compliance certifications
   • DAn open-source deep learning framework deployed on on-premises GPU servers
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6. 25application · medium

   A bank is integrating an AI-powered chatbot with its customer service system. The chatbot will access customer account information. The program manager must ensure that customer data is protected and that the integration complies with privacy regulations. Which measure should be implemented?

   • ADisable logging for all chatbot interactions to protect customer privacy
   • BAllow the chatbot to access all customer data to improve response accuracy
   • CUse encrypted API connections and implement role-based access control for the chatbot's data access
   • DStore customer data in the chatbot vendor's cloud for faster processing
   Reveal Solution Discussion Select an answer first