Microsoft Certified: Azure AI Engineer AssociatePlan and manage an Azure AI solutionHard
A manufacturing company is developing an Azure AI solution for predictive maintenance, analyzing telemetry data from factory machines. The solution uses Azure Machine Learning for model training and deployment. To ensure that the AI solution can continue operating even if one Azure region experiences a major outage, what architectural principle should be applied for high availability and disaster recovery?
- AApplication-level retry logic
- BGeo-redundancy across multiple paired regions
- CLocal redundancy within a single datacenter
- DZone redundancy within a single region
Show answer & explanationAnswer & explanation
Correct answer: B. Geo-redundancy across multiple paired regions
Geo-redundancy across multiple paired regions ensures that if an entire Azure region becomes unavailable, the AI solution's resources and data are replicated to another geographic region, allowing for recovery and continued operation with minimal downtime.
Why the other options are wrong
- A. Application-level retry logic handles transient errors but does not provide architectural resilience against major regional outages.
- C. Local redundancy protects against localized hardware failures within a single datacenter, offering the lowest level of protection.
- D. Zone redundancy protects against datacenter-level failures within a single region, but not against an entire region outage.
Azure Geo-redundancy
A strategy for high availability and disaster recovery that involves replicating data and resources across two or more geographically separate Azure regions.
- Protects against entire region outages.
- Ensures business continuity for mission-critical applications.
- Often involves paired regions for data replication.
Memory trick: Geo-redundancy is like having a 'twin city' for your entire Azure setup.