DP-203 · Question #93
DP-203 Question #93: Real Exam Question with Answer & Explanation
The correct answer is C: Scale out the query by using PARTITION BY.. C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning divide data into subsets based on a partition key. A process that consumes the data (such as a Analytics job) can consume and write different partitions in parallel, which increas
Question
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID. You monitor the Stream Analytics job and discover high latency. You need to reduce the latency. Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
Options
- AAdd a pass-through query.
- BAdd a temporal analytic function.
- CScale out the query by using PARTITION BY.
- DConvert the query to a reference query.
- EIncrease the number of streaming units.
Explanation
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning divide data into subsets based on a partition key. A process that consumes the data (such as a Analytics job) can consume and write different partitions in parallel, which increases throughput. E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner. https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-
Community Discussion
No community discussion yet for this question.