DP-600 · Question #210
When monitoring data flow performance, which of the following should be looked for possible bottlenecks? (Select Two)
The correct answer is A. Cluster start-up time C. Transformation time. Cluster start-up time is a significant potential bottleneck because it involves the time required to spin up an Apache Spark cluster. Data flows operate on a just-in-time model where each job uses an isolated cluster, and this start-up time typically takes 3-5 minutes. Reducing t
Question
When monitoring data flow performance, which of the following should be looked for possible bottlenecks? (Select Two)
Options
- ACluster start-up time
- BNetwork latency
- CTransformation time
- DMemory usage
How the community answered
(33 responses)- A94% (31)
- B3% (1)
- D3% (1)
Explanation
Cluster start-up time is a significant potential bottleneck because it involves the time required to spin up an Apache Spark cluster. Data flows operate on a just-in-time model where each job uses an isolated cluster, and this start-up time typically takes 3-5 minutes. Reducing this start-up time can improve overall data flow performance Transformation time is another critical bottleneck to monitor. It refers to the time taken for each transformation stage in the data flow. If a transformation stage takes the longest time, it indicates a need for optimization, such as repartitioning data or increasing the integration runtime size to enhance performance. https://learn.microsoft.com/en-us/azure/data-factory/concepts-data-flow-performance
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