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Answer: Use idempotent operations and configure the job with a checkpoint interval.
To ensure exactly once delivery in Azure Stream Analytics, you should use idempotent operations, which allow the system to process the same data multiple times without causing duplicate entries. Additionally, configuring the job with a checkpoint interval helps in recovering from failures by ensuring that the job can resume from the last checkpoint without reprocessing data.
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You are tasked with designing an Azure Stream Analytics job to process real-time data from IoT devices. The data includes sensor readings that must be processed exactly once to avoid duplicate entries in the downstream database. How would you configure Azure Stream Analytics to ensure exactly once delivery, and what steps would you take to handle potential issues such as network failures or system crashes?
A
Use idempotent operations and configure the job with a checkpoint interval.
B
Increase the number of streaming units and use a retry policy.
C
Enable multiple output streams and use a failover mechanism.
D
Set up a separate validation job to check for duplicates.
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