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Answer: Setting up Azure Monitor with Application Insights to track performance metrics and dependencies in real-time, utilizing live metrics stream
Choosing to set up Azure Monitor with Application Insights is the optimal approach for several reasons: 1. **Comprehensive Monitoring**: Azure Monitor offers a holistic view of your application's performance and health, with Application Insights specifically designed to pinpoint issues swiftly. 2. **Real-time Metrics and Dependencies**: The live metrics stream feature allows for the tracking of crucial performance indicators like CPU and memory usage in real-time, enabling the immediate detection of anomalies. 3. **Immediate Issue Identification**: The real-time monitoring capability ensures that performance issues can be identified and addressed as they occur, which is vital for maintaining the efficiency of real-time data processing pipelines. 4. **Scalability and Flexibility**: This solution is adept at handling large data volumes, making it suitable for complex, real-time processing scenarios. In summary, Azure Monitor with Application Insights stands out as the most effective method for diagnosing intermittent performance issues in real-time data processing pipelines, thanks to its real-time monitoring, scalability, and flexibility.
Author: LeetQuiz Editorial Team
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When encountering complex, intermittent performance issues in a real-time data processing pipeline utilizing Azure Databricks and Azure Event Hubs, which strategy would you employ for real-time diagnosis?
A
Implementing custom telemetry in the Databricks notebooks and Event Hubs capture functions to log detailed performance data to Azure Log Analytics
B
Using the Databricks Spark UI and Event Hubs metrics in Azure Portal for manual correlation of performance issues
C
Setting up Azure Monitor with Application Insights to track performance metrics and dependencies in real-time, utilizing live metrics stream
D
Streaming system and application metrics to Azure Time Series Insights for real-time analysis and anomaly detection
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