10 Azure Application Insights Interview Questions and Answers

Azure Application Insights is a powerful tool for monitoring and analyzing the performance and usage of your applications. It provides deep insights into application health, enabling developers and IT professionals to diagnose issues, track application performance, and understand user behavior. Integrated seamlessly with the Azure ecosystem, it supports a wide range of platforms and languages, making it a versatile choice for modern cloud-based applications.

This article offers a curated selection of interview questions designed to test your knowledge and expertise in Azure Application Insights. By reviewing these questions and their detailed answers, you will be better prepared to demonstrate your proficiency and understanding of this essential monitoring tool during your interview.

Azure Application Insights Interview Questions and Answers

1. How would you configure custom telemetry in Application Insights? Provide a code example.

Custom telemetry in Azure Application Insights allows you to track specific events, metrics, or logs that are not automatically captured. This is useful for monitoring specific business logic or application performance metrics unique to your application. To configure custom telemetry, use the Application Insights SDK to send custom events, metrics, or traces.

Here is a simple example in Python:

from applicationinsights import TelemetryClient

# Initialize the TelemetryClient with your Instrumentation Key
tc = TelemetryClient('your_instrumentation_key')

# Track a custom event
tc.track_event('CustomEvent', {'property1': 'value1', 'property2': 'value2'})

# Track a custom metric
tc.track_metric('CustomMetric', 42)

# Track a custom trace
tc.track_trace('CustomTrace', {'property1': 'value1'})

# Send the telemetry data
tc.flush()

2. How to track dependencies in Application Insights? Provide a code snippet to illustrate your answer.

In Azure Application Insights, dependencies are external resources your application interacts with, such as databases or REST APIs. Tracking these dependencies helps monitor the performance and availability of these external systems, aiding in diagnosing issues and improving application reliability.

To track dependencies, use the Application Insights SDK. Below is a code snippet demonstrating how to track a dependency in a .NET application:

using Microsoft.ApplicationInsights;
using Microsoft.ApplicationInsights.DataContracts;

public class DependencyTracker
{
    private TelemetryClient telemetryClient;

    public DependencyTracker()
    {
        telemetryClient = new TelemetryClient();
    }

    public void TrackDependency()
    {
        var dependencyTelemetry = new DependencyTelemetry
        {
            Name = "SampleDependency",
            Target = "sample.target.com",
            Data = "GET /api/sample",
            Type = "HTTP",
            Timestamp = DateTime.UtcNow,
            Duration = TimeSpan.FromMilliseconds(123),
            Success = true
        };

        telemetryClient.TrackDependency(dependencyTelemetry);
    }
}

In this example, the DependencyTelemetry object creates a new dependency telemetry item. The TelemetryClient then tracks this dependency, sending the telemetry data to Application Insights.

3. Describe how you can create custom metrics in Application Insights. Include a code example.

Custom metrics in Azure Application Insights allow you to track specific data points relevant to your application. These metrics provide insights into performance and usage beyond standard telemetry data. To create custom metrics, use the Application Insights SDK to send custom events or metrics to your Application Insights resource.

Here is a concise example using the Application Insights SDK for Python:

from applicationinsights import TelemetryClient

# Initialize the TelemetryClient with your instrumentation key
tc = TelemetryClient('your_instrumentation_key')

# Track a custom metric
tc.track_metric('custom_metric_name', 42)

# Flush the data to ensure it is sent to Application Insights
tc.flush()

In this example, the TelemetryClient is initialized with your Application Insights instrumentation key. The track_metric method sends a custom metric named ‘custom_metric_name’ with a value of 42. Finally, the flush method ensures the data is sent to Application Insights.

4. How would you set up alerts based on specific telemetry data?

Azure Application Insights is a tool for monitoring the performance and usage of your applications. It provides telemetry data such as request rates, response times, and failure rates, which can be important for maintaining application health. Setting up alerts based on specific telemetry data allows you to address issues before they impact users.

To set up alerts in Azure Application Insights, follow these steps:

• Navigate to the Azure portal and select your Application Insights resource.
• In the left-hand menu, click on “Alerts” under the “Monitoring” section.
• Click on “New alert rule” to create a new alert.
• Define the scope by selecting the Application Insights resource you want to monitor.
• Choose a condition by selecting the specific telemetry data you want to base the alert on, such as failed requests, server response time, or custom metrics.
• Configure the alert logic by setting thresholds and frequency for the alert.
• Define the action group, which specifies who should be notified and how (e.g., email, SMS, webhook).
• Review and create the alert rule.

5. Explain the difference between TrackEvent, TrackTrace, and TrackException methods. Provide examples of when to use each.

In Azure Application Insights, the methods TrackEvent, TrackTrace, and TrackException are used for different types of telemetry data.

• TrackEvent: This method logs custom events. Events track user actions or other significant occurrences in your application. For example, use TrackEvent to log when a user completes a purchase or when a specific feature is used.
• TrackTrace: This method logs diagnostic information. Traces capture detailed information about the application’s execution, useful for debugging and monitoring. For example, use TrackTrace to log the flow of execution through your application or capture variable values at specific points in time.
• TrackException: This method logs exceptions. Exceptions capture errors and other unexpected conditions during application execution. For example, use TrackException to log an error when a user tries to access a resource that does not exist.

Example:

from applicationinsights import TelemetryClient

tc = TelemetryClient('your_instrumentation_key')

# Track a custom event
tc.track_event('UserSignedUp', {'UserId': '12345'}, {'SignUpTime': 1})

# Track a trace
tc.track_trace('User navigated to dashboard', {'UserId': '12345'})

# Track an exception
try:
    1 / 0
except ZeroDivisionError as e:
    tc.track_exception()

6. How can you filter out specific telemetry data from being sent? Provide a code example.

In Azure Application Insights, you can filter out specific telemetry data from being sent by using telemetry processors. Telemetry processors allow you to inspect and modify telemetry data before it is sent to the Application Insights service. This is useful for excluding certain types of telemetry data that you do not want to track, such as specific requests or dependencies.

Here is a code example of how to implement a custom telemetry processor in C# to filter out telemetry data:

using Microsoft.ApplicationInsights.Channel;
using Microsoft.ApplicationInsights.Extensibility;

public class CustomTelemetryProcessor : ITelemetryProcessor
{
    private ITelemetryProcessor Next { get; set; }

    public CustomTelemetryProcessor(ITelemetryProcessor next)
    {
        this.Next = next;
    }

    public void Process(ITelemetry item)
    {
        // Example: Filter out all telemetry data with a specific property
        if (item.Context.GlobalProperties.ContainsKey("ExcludeTelemetry"))
        {
            return; // Do not send this telemetry item
        }

        this.Next.Process(item); // Send the telemetry item
    }
}

// In ApplicationInsights.config or during initialization
TelemetryConfiguration.Active.TelemetryProcessorChainBuilder
    .Use(next => new CustomTelemetryProcessor(next))
    .Build();

In this example, the CustomTelemetryProcessor class implements the ITelemetryProcessor interface. The Process method checks if the telemetry item contains a specific property (ExcludeTelemetry). If it does, the telemetry item is not sent; otherwise, it is passed to the next processor in the chain.

7. How would you implement distributed tracing? Provide a detailed explanation.

Distributed tracing is a method used to track and observe service requests as they flow through various components of a distributed system. It helps in identifying performance bottlenecks, understanding service dependencies, and diagnosing issues in microservices architectures. Azure Application Insights provides built-in support for distributed tracing, making it easier to monitor and diagnose issues across different services.

To implement distributed tracing in Azure Application Insights, follow these steps:

• Instrument Your Application: Ensure that all your microservices are instrumented with Application Insights SDK. This involves adding the SDK to your application and configuring it with the appropriate instrumentation key.
• Enable Distributed Tracing: Configure the Application Insights SDK to enable distributed tracing. This typically involves setting up correlation IDs that are passed along with each request to track the flow across services.
• Configure Telemetry Initializers: Use telemetry initializers to add custom properties to your telemetry data. This can help in identifying and filtering specific traces.
• Integrate with Other Services: If your application interacts with other Azure services (e.g., Azure Functions, Azure Service Bus), ensure that these services are also configured to use Application Insights for telemetry.
• Monitor and Analyze Traces: Use the Application Insights portal to monitor and analyze the collected traces. The portal provides various tools and visualizations to help you understand the flow of requests and identify any issues.

8. How does Application Insights handle sampling, and why is it important?

Application Insights handles sampling by selectively collecting a subset of telemetry data to reduce the volume of data sent to the Application Insights service. This is particularly important for applications with high traffic, as it helps manage costs and ensures that the system remains performant without overwhelming the storage and processing capabilities.

There are two main types of sampling in Application Insights:

• Adaptive Sampling: This dynamically adjusts the sampling rate based on the volume of incoming telemetry. When the traffic is high, the sampling rate increases to reduce the amount of data collected. Conversely, when the traffic is low, the sampling rate decreases to collect more data. This ensures that the data collected is representative of the overall traffic patterns.
• Fixed-rate Sampling: This applies a constant sampling rate regardless of the traffic volume. It is useful when you want a consistent sampling rate for all telemetry data.

Sampling is important because it helps balance the need for detailed telemetry data with the practical constraints of storage and cost. By reducing the amount of data collected, sampling ensures that the Application Insights service remains responsive and cost-effective while still providing valuable insights into application performance and user behavior.

9. What are the different types of telemetry data collected?

Azure Application Insights collects various types of telemetry data to help monitor and diagnose the performance and usage of applications. The main types of telemetry data collected include:

• Request Telemetry: This tracks the incoming requests to your application, including the URL, response time, and success/failure status.
• Trace Telemetry: This captures diagnostic information such as log messages, which can help in understanding the flow and state of the application.
• Exception Telemetry: This records unhandled exceptions and errors that occur in your application, providing stack traces and error messages for debugging purposes.
• Dependency Telemetry: This monitors external calls made by your application, such as database queries or HTTP requests to other services, including their response times and success/failure status.
• Event Telemetry: This logs custom events that you define, which can be used to track specific actions or occurrences within your application.
• Metric Telemetry: This collects numerical data points over time, such as CPU usage, memory consumption, or custom metrics that you define.
• Availability Telemetry: This checks the availability and responsiveness of your application by running synthetic tests at regular intervals.

10. Explain the concept of adaptive sampling.

Adaptive sampling in Azure Application Insights is a technique used to automatically adjust the rate at which telemetry data is collected and sent to the Application Insights service. This is particularly useful for high-traffic applications where the volume of telemetry data can become overwhelming, leading to increased costs and potential performance issues.

The primary objective of adaptive sampling is to ensure that the data collected is representative of the overall application performance and user experience, without sending every single telemetry event. It dynamically adjusts the sampling rate based on the volume of incoming telemetry data. When the traffic is low, it collects more data, and when the traffic is high, it reduces the amount of data collected.

Adaptive sampling works by:

• Monitoring the rate of incoming telemetry data.
• Adjusting the sampling rate to maintain a manageable volume of data.
• Ensuring that the sampled data is statistically representative of the overall traffic.

This approach helps in maintaining a balance between data accuracy and resource utilization, making it an efficient way to handle telemetry data in large-scale applications.