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Answer: Utilize Azure Databricks' autoscaling feature to dynamically adjust resources based on workload, with real-time monitoring to ensure latency SLAs are met while optimizing for cost.
The optimal solution is to leverage Azure Databricks' autoscaling feature, which dynamically adjusts resources in response to workload changes. This approach ensures that latency SLAs are met during peak times by scaling up resources and reduces costs during off-peak times by scaling down. Real-time monitoring is essential to validate that performance standards are consistently met. Options A and B represent extremes that either compromise on performance or cost, while option D is inefficient and not scalable for dynamic workloads.
Author: LeetQuiz Editorial Team
In the context of designing a production streaming system on Azure Databricks, you are tasked with ensuring the system adheres to strict latency SLAs while also being cost-effective. The system must dynamically adjust to varying workloads without manual intervention. Considering these requirements, which of the following strategies would BEST meet both the performance and cost-efficiency goals? (Choose one option)
A
Minimize costs by under-provisioning resources, accepting that latency SLAs may occasionally not be met during peak loads.
B
Over-provision resources to guarantee latency SLAs are always met, regardless of the cost implications.
C
Utilize Azure Databricks' autoscaling feature to dynamically adjust resources based on workload, with real-time monitoring to ensure latency SLAs are met while optimizing for cost.
D
Manually adjust resources based on predicted workload changes, which may lead to either under-utilization or over-utilization of resources.
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