DP-203 · Question #126
A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream Analytics cloud job to analyze the data. The…
The correct answer is D. Scale the SU count for the job up. F. Implement query parallelization by partitioning the data input. D: Scale out the query by allowing the system to process each input partition separately. F: A Stream Analytics job definition includes inputs, a query, and output. Inputs are where the job reads the data stream from…
Question
A company has a real-time data analysis solution that is hosted on Microsoft Azure. The solution uses Azure Event Hub to ingest data and an Azure Stream Analytics cloud job to analyze the data. The cloud job is configured to use 120 Streaming Units (SU). You need to optimize performance for the Azure Stream Analytics job. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Options
- AImplement event ordering.
- BImplement Azure Stream Analytics user-defined functions (UDF).
- CImplement query parallelization by partitioning the data output.
- DScale the SU count for the job up.
- EScale the SU count for the job down.
- FImplement query parallelization by partitioning the data input.
How the community answered
(40 responses)- A5% (2)
- B3% (1)
- C10% (4)
- D80% (32)
- E3% (1)
Explanation
D: Scale out the query by allowing the system to process each input partition separately. F: A Stream Analytics job definition includes inputs, a query, and output. Inputs are where the job reads the data stream from. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
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