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Answer: Simulating peak loads using test data generated by Azure Load Testing and monitoring the pipeline with Azure Monitor
1. **Simulating peak loads**: Utilizing test data generated by Azure Load Testing allows for the simulation of peak data volumes, accurately replicating conditions that cause latency. This method helps identify bottlenecks that only appear under peak loads. 2. **Monitoring with Azure Monitor**: Tracking the pipeline with Azure Monitor during the test enables real-time observation of key performance metrics like throughput, latency, and resource utilization, pinpointing exact components causing latency. 3. **Comprehensive testing approach**: This strategy tests the end-to-end performance of the pipeline under realistic conditions, ensuring optimization focuses on the pipeline as a whole rather than isolated components. This approach is the most effective for identifying and optimizing bottlenecks in Azure Databricks streaming data pipelines.
Author: LeetQuiz Editorial Team
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How would you design a performance test to identify and optimize bottlenecks in your Azure Databricks streaming data pipeline experiencing latency under peak data volumes?
A
Incrementally increasing the data volume in a development environment until performance issues arise
B
Analyzing historical performance data to predict potential bottlenecks without actual testing
C
Simulating peak loads using test data generated by Azure Load Testing and monitoring the pipeline with Azure Monitor
D
Only testing the individual components of the pipeline for latency, without considering end-to-end performance
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