AZ-400 Question 299
Select 2You have a .NET web application hosted on Azure App Service with Application Insights enabled. The team recently released a new feature and wants to understand how many users interact with it, how much time they spend on each step, and whether there are performance bottlenecks. You are tasked with gathering and analyzing this telemetry to provide usage metrics and performance data for the new feature. Which two actions should you take to collect and analyze the relevant metrics using Application Insights?
- A
A. Tag requests for the new feature with custom properties and then query them using Application Insights Logs (KQL).
- B
B. Configure an Application Insights Availability Test to simulate user interactions and measure overall uptime.
- C
C. Enable adaptive sampling for all telemetry items to automatically reduce the volume of data collected.
- D
D. Add custom events that specifically track user flow steps in the new feature, and analyze those events in the Usage blade.
- E
E. Enable Live Metrics Stream to automatically capture complete end-to-end traces for the new feature.
Show answer and explanation
Correct answers: A, D
Explanation
To analyze usage and application performance for a newly released feature in a production environment, you typically combine custom properties or events with Application Insights queries. By tagging requests (Option A) and instrumenting user flow steps with custom events (Option D), you can query this data in the Application Insights Logs (KQL) blade or the Usage blade to identify how often users engage with the feature and where potential bottlenecks arise. Refer to official documentation (e.g., https://learn.microsoft.com/azure/azure-monitor/app/usage-overview) to understand how to configure and analyze custom events, properties, and Kusto queries for more actionable insights.
- A. Correct.
A. Tag requests for the new feature with custom properties and then query them using Application Insights Logs (KQL): Correct. Tagging relevant requests with custom properties (e.g., feature name) allows you to filter and query those requests for usage metrics and performance analysis. By running Kusto (KQL) queries in the Logs blade, you can drill deeper into user interactions specifically tied to the new feature.
- B. Incorrect.
B. Configure an Application Insights Availability Test to simulate user interactions and measure overall uptime: Incorrect. Availability Tests are synthetic checks that help measure availability and basic health, not real end-user interactions or usage metrics. They do not effectively capture detailed user flow or average time spent in feature steps.
- C. Incorrect.
C. Enable adaptive sampling for all telemetry items to automatically reduce the volume of data collected: Incorrect. Adaptive sampling is useful for reducing telemetry costs by sampling high-volume telemetry, but it does not directly address collecting new feature usage metrics or performance data. Sampling helps manage overhead but doesn�t inherently track feature-specific events.
- D. Correct.
D. Add custom events that specifically track user flow steps in the new feature, and analyze those events in the Usage blade: Correct. Defining custom events for your new feature� user flow steps allows you to capture user interactions (e.g., entering step 1, completing step 2). The Application Insights Usage blade and Analytics queries can then reveal how often these steps are used, the time spent, and any drop-offs.
- E. Incorrect.
E. Enable Live Metrics Stream to automatically capture complete end-to-end traces for the new feature: Incorrect. Live Metrics Stream provides near real-time metrics of incoming requests, dependencies, exceptions, etc., primarily useful for quickly troubleshooting production issues. It is not the best way to track detailed usage or user flow metrics over time for the new feature.