Determine the Use of AI On-Premises, in the Cloud, and at the Edge CERTIFIED-AI-EXPERT Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A manufacturing company wants to deploy a computer vision model on factory floor cameras to detect defective products in real time. The factory has intermittent internet connectivity, and sending video to a central server would introduce unacceptable latency. Which deployment environment is most appropriate?

   • ACloud AI with a managed vision service
   • BHybrid: edge for inference, cloud for retraining
   • CEdge AI on local devices at each camera
   • DOn-premises AI on a central factory server
   Reveal Solution Discussion Select an answer first
2. 2application · medium

   A financial services firm is developing an AI model that analyzes proprietary trading algorithms. The model must be trained on historical market data that is subject to strict data residency regulations, and the firm already operates a high-performance computing cluster with idle capacity. The training workload is predictable and runs nightly. Which deployment environment best satisfies the firm's requirements?

   • AOn-premises AI using the existing HPC cluster
   • BCloud AI with a managed ML platform in a compliant region
   • CHybrid: train in cloud, store data on-premises
   • DEdge AI deployed on trading floor workstations
   Reveal Solution Discussion Select an answer first
4. 3expert · hard

   A retail chain is deploying an AI model for inventory forecasting. The model needs to process data from hundreds of stores and update forecasts daily. The company has a limited IT team and wants to minimize upfront capital expenditure. However, the company also has a legacy on-premises data warehouse that stores historical sales data. Which deployment strategy is most cost-effective and manageable?

   • AUse edge AI at each store
   • BUse on-premises AI with the existing data warehouse
   • CUse on-premises AI for training and cloud for inference
   • DUse cloud AI for model training and inference, with data replicated to the cloud
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which characteristic is most associated with edge AI deployment?

   • AProcessing data near the source to reduce latency
   • BFull control over hardware and data within an organization's own facility
   • CReliance on a continuous internet connection to function
   • DHigh scalability through elastic resource provisioning
   Reveal Solution Discussion Select an answer first
6. 5application · medium

   A startup is developing a natural language processing application that needs to process millions of customer reviews to extract sentiment trends. The startup has no on-premises infrastructure and wants to launch quickly. The processing is batch-oriented and can tolerate some latency. Which deployment environment is most appropriate?

   • AEdge AI on customer devices
   • BHybrid: on-premises for storage, cloud for processing
   • COn-premises AI with purchased servers
   • DCloud AI with a managed NLP service
   Reveal Solution Discussion Select an answer first