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Answer: Connect to Azure Application Insights using its SDK to gather and examine custom telemetry directly from notebooks.
Integrating with Azure Application Insights via its SDK is the optimal strategy for capturing and analyzing custom telemetry from Databricks notebooks. This approach allows for the detailed monitoring of latency and errors, essential for microservices calling external APIs. Azure Application Insights supports custom metrics and events, offering the granularity needed for effective performance tracking. Its scalability ensures it can manage the telemetry data volume from multiple microservices, facilitating real-time analysis and troubleshooting without compromising performance. This method stands out for its flexibility, scalability, and the depth of monitoring it provides, making it the best choice for ensuring the reliability and efficiency of microservices in Databricks.
Author: LeetQuiz Editorial Team
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How can you effectively monitor and analyze high-resolution metrics for microservices within Databricks notebooks that interact with external APIs, focusing on latency and error tracking?
A
Leverage Databricks event logs enhanced with custom tags for microservices, enabling detailed performance analysis in Azure Log Analytics.
B
Implement custom logging within notebooks and utilize Databricks' native logging features for analysis.
C
Connect to Azure Application Insights using its SDK to gather and examine custom telemetry directly from notebooks.
D
Transfer logs to a specialized Azure Monitor log analytics workspace, optimized for high-resolution metrics.
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