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Choose the best response for each of the following questions.
Contoso's fraud detection AI system must maintain high availability to meet regulatory requirements for real-time transaction processing across North America and Europe. The system serves 50,000 inference requests per second during peak hours with model updates deployed weekly. Network latency must stay under 50 milliseconds for users in both regions. Which Microsoft Foundry hub deployment pattern meets these requirements most cost-effectively?
Deploy a single hub in East US with compute clusters scaled to handle global traffic, using Azure Front Door to route requests efficiently across continents
Deploy active-active hubs in East US and West Europe with full compute capacity in both regions, configuring Azure Traffic Manager to route requests to the nearest hub based on user location
Deploy a primary hub in East US with an active-passive secondary hub in West Europe, keeping the secondary hub without compute resources until failover occurs to reduce costs
Your AI team stores 50 TB of training datasets in Azure Blob Storage that took eight months to collect and label from customer transactions. The datasets undergo incremental updates monthly with approximately 2 TB of new data. Compliance regulations require the ability to restore accidentally deleted data within 48 hours. The monthly training jobs can tolerate up to 30 minutes of lost progress if the storage region fails. Which storage configuration provides appropriate protection at the lowest cost?
LRS with 365-day soft delete retention and daily backups to a separate storage account in a different region using AzCopy scheduled tasks
GRS with 30-day soft delete retention and a CanNotDelete resource lock, accepting the 15-minute asynchronous replication lag as within acceptable data loss tolerance
GZRS with 90-day soft delete retention to protect against both zone-level and region-level failures, ensuring maximum durability for irreplaceable training datasets
Contoso deploys sentiment analysis models packaged as 8 GB Docker containers that update twice daily as new training data becomes available. The containers deploy to Microsoft Foundry compute clusters in East US (primary) and West US (failover). During the last deployment, West US clusters failed to pull the updated container for 12 minutes after the East US deployment succeeded, causing version inconsistency during that window. How should you optimize the container registry configuration to minimize version drift between regions?
Switch from Azure Container Registry Premium to Standard tier in both regions with manual image push to each regional registry immediately after building to ensure simultaneous availability
Continue using ACR Premium with geo-replication but implement a deployment pipeline that polls the West US replica API every 30 seconds for image availability before updating the West US hub's compute clusters
Upgrade to ACR Premium with zone-redundant storage in the primary region and increase the number of replicas to three regions including Central US as an intermediary replication hop
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