Answer: Deploy identical Azure Stream Analytics jobs to paired regions in Azure.

The question specifically asks about improving high availability for the **real-time data processing solution**, not just data storage. Let's evaluate each option: **Option A (Deploy a High Concurrency Databricks cluster)**: This improves performance and concurrency for analytical workloads but does not inherently provide high availability for real-time data processing. Databricks clusters can still fail, and this approach doesn't address regional redundancy or automatic failover capabilities. **Option B (Deploy an Azure Stream Analytics job and use Azure Automation runbook)**: While this provides some level of monitoring and restart capability, it's a reactive approach rather than a true high availability solution. There would still be downtime during job restart, and it doesn't provide regional redundancy. **Option C (Set Data Lake Storage to use geo-redundant storage)**: This only addresses data storage redundancy, not processing availability. GRS ensures data durability across regions but does nothing to maintain the real-time processing pipeline if the primary region fails. **Option D (Deploy identical Azure Stream Analytics jobs to paired regions)**: This is the optimal solution because: - Azure paired regions are specifically designed for disaster recovery and high availability - It provides active-active or active-passive redundancy for the processing pipeline - If one region fails, the paired region can immediately take over processing - This approach maintains continuous real-time data processing with minimal disruption - It aligns with Azure's regional pairing strategy for business continuity The key distinction is that high availability for real-time data processing requires redundancy in the processing engine itself (Azure Stream Analytics), not just in data storage or monitoring mechanisms.

Author: LeetQuiz Editorial Team