AZ-400 Designing and Implementing Microsoft DevOps Solutions Exam

Configure and Query Development Telemetry

Development telemetry involves tracking and analyzing data related to the performance and behavior of your application during development. In Azure, tools like Azure Monitor and Application Insights provide robust mechanisms for capturing telemetry data.

Key Performance Indicators (KPIs)

Selecting KPIs is crucial for monitoring the health and performance of your application. These indicators help you understand usage patterns, performance bottlenecks, and error rates. Common KPIs include:

• Response Time: Measures how long it takes for the application to process a request.
• Error Rate: Tracks the number of errors occurring over a period.
• Resource Utilization: Monitors CPU, memory, and network usage.

Configuration in Azure Monitor

Azure Monitor helps manage and analyze telemetry collected from cloud and on-premises environments. To configure telemetry, you can:

• Create a diagnostic setting: Route valuable log data to an Azure Log Analytics workspace.
• Use resource-specific logging: Tailor log settings to specific resources like AKS clusters.
• Adjust log collection tiers: Optimize cost by using basic logs for less frequently queried data.

Application Insights

Application Insights specifically targets application performance monitoring. Key configuration steps include:

• Enable workspace-based Application Insights: Provides enhanced tools for cost management.
• Use sampling: Reduces telemetry volume while maintaining metric accuracy.
• Preaggregate metrics: Minimizes data traffic by aggregating metrics before sending them.

Kusto Query Language (KQL)

KQL is used to query and analyze telemetry data in Azure Monitor and Application Insights. Best practices for KQL include:

• Use the where operator: Reduce the amount of processed data by filtering queries early.
• Optimize string operations: Prefer has over contains for better performance.
• Materialize frequent expressions: Improve query speed by using the materialize() function with let statements.

Analyzing Results

Analyzing telemetry data helps pinpoint development bottlenecks and improve application performance. Important steps include:

• Creating custom metrics and log tables: Tailor Azure Monitor to specific application needs.
• Running KQL queries: Surface insights on application performance issues and pipeline stability.
• Setting alerts: Proactively manage application health by configuring alerts based on telemetry data thresholds.

In summary, configuring and querying development telemetry using tools like Azure Monitor, Application Insights, and Kusto Query Language enables detailed insights into application performance. By following best practices and focusing on key performance metrics, developers can ensure their applications run optimally and efficiently.