What should you do to improve high availability of the real-time data processing solution?

A. Deploy a High Concurrency Databricks cluster.

B. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.

C. Set Data Lake Storage to use geo-redundant storage (GRS).

D. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.








 

Suggested Answer: D

Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure's paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability

This question is in DP-203 Data Engineering on Microsoft Azure Exam
For getting Microsoft Certified: Azure Data Engineer Associate Certificate


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