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AZ-400 · topic practice

Implement an instrumentation strategy practice questions

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Yes — use the session launcher on this page to start a 10-, 20-, 30- or 50-question session drawn entirely from the Implement an instrumentation strategy domain.

Practise Microsoft Azure DevOps Engineer Expert AZ-400 Implement an instrumentation strategy practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Implement an instrumentation strategy

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What the exam tests

What to know about Implement an instrumentation strategy

Implement an instrumentation strategy questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Implement an instrumentation strategy exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Implement an instrumentation strategy questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full network assurance explanation →

You are configuring Application Insights for a .NET Core web application deployed to Azure App Service. The application must capture telemetry for all HTTP requests, exceptions, and dependency calls with minimal code changes. What should you do?

Trap 1: Configure diagnostics logging in the App Service and stream logs to…

Diagnostics logging captures only logs, not full telemetry.

Trap 2: Install the Microsoft.ApplicationInsights.AspNetCore NuGet package…

This requires code changes, which the scenario wants to minimize.

Trap 3: Add the Application Insights JavaScript SDK to each page.

This only captures client-side telemetry, not server-side.

Study all Implement an instrumentation strategy common traps →

A
Enable the Application Insights site extension in the App Service 'Application Insights' blade.
The site extension enables auto-instrumentation with no code changes.
B
Configure diagnostics logging in the App Service and stream logs to Application Insights.
Why wrong: Diagnostics logging captures only logs, not full telemetry.
C
Install the Microsoft.ApplicationInsights.AspNetCore NuGet package and add services.AddApplicationInsightsTelemetry() in Startup.cs.
Why wrong: This requires code changes, which the scenario wants to minimize.
D
Add the Application Insights JavaScript SDK to each page.
Why wrong: This only captures client-side telemetry, not server-side.

Full breakdown with real-world context →

Question 2mediummultiple choice

Read the full network assurance explanation →

Your team uses Azure DevOps for CI/CD. You need to ensure that every build publishes telemetry to Application Insights, including build duration, test pass rate, and deployment frequency. Which approach should you use?

Trap 1: Call the Azure DevOps REST API from a custom script in the pipeline…

Possible but not the recommended approach; Analytics is built for this.

Trap 2: Run the Azure DevOps CLI command 'az devops telemetry publish' in a…

No such CLI command exists.

Trap 3: Use the built-in 'Pipeline Telemetry' dashboard in Azure DevOps.

Built-in charts are pre-defined and cannot be customized.

Study all Implement an instrumentation strategy common traps →

A
Call the Azure DevOps REST API from a custom script in the pipeline to capture telemetry.
Why wrong: Possible but not the recommended approach; Analytics is built for this.
B
Run the Azure DevOps CLI command 'az devops telemetry publish' in a build task.
Why wrong: No such CLI command exists.
C
Use the built-in 'Pipeline Telemetry' dashboard in Azure DevOps.
Why wrong: Built-in charts are pre-defined and cannot be customized.
D
Use the Azure DevOps Analytics OData endpoint to query pipeline telemetry and send to Application Insights via a release task.
OData provides structured telemetry data that can be streamed.

Full breakdown with real-world context →

Question 3hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

You are designing a centralized logging strategy for multiple microservices hosted in Azure Kubernetes Service (AKS). Each microservice writes logs in JSON format to stdout/stderr. The operations team needs to query logs across all services and correlate them with application performance metrics. Which solution provides the best integration?

Trap 1: Configure AKS to send logs to Azure Blob Storage and use Azure…

Blob Storage doesn't support efficient log querying.

Trap 2: Stream logs to Azure Event Hubs and then to Azure Data Explorer for…

Event Hubs is a transport, not a storage/query solution.

Trap 3: Deploy the Application Insights agent as a DaemonSet in AKS and…

Application Insights is not designed for bulk log ingestion.

Study all Implement an instrumentation strategy common traps →

A
Configure AKS to send logs to Azure Blob Storage and use Azure Storage Analytics for querying.
Why wrong: Blob Storage doesn't support efficient log querying.
B
Enable Container Insights in Azure Monitor to collect stdout/stderr logs and metrics into a Log Analytics workspace.
Container Insights provides integrated log and metric collection with querying via KQL.
C
Stream logs to Azure Event Hubs and then to Azure Data Explorer for analysis.
Why wrong: Event Hubs is a transport, not a storage/query solution.
D
Deploy the Application Insights agent as a DaemonSet in AKS and send logs directly to Application Insights.
Why wrong: Application Insights is not designed for bulk log ingestion.

Full breakdown with real-world context →

Question 4mediummultiple choice

Read the full Implement an instrumentation strategy explanation →

You have an Azure DevOps pipeline that deploys a web app to Azure App Service. You want to capture deployment frequency and change failure rate as metrics in Application Insights. Which built-in analytics view should you use?

Trap 1: Application Dashboard

General overview, not specific to deployment metrics.

Trap 2: Time to Restore Service

This tracks MTTR, not deployment frequency.

Trap 3: Azure DevOps Pipeline Telemetry

Not a standard analytics view in Application Insights.

Study all Implement an instrumentation strategy common traps →

A
Deployment Frequency
It provides the requested metrics.
B
Application Dashboard
Why wrong: General overview, not specific to deployment metrics.
C
Time to Restore Service
Why wrong: This tracks MTTR, not deployment frequency.
D
Azure DevOps Pipeline Telemetry
Why wrong: Not a standard analytics view in Application Insights.

Full breakdown with real-world context →

Question 5hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

You are troubleshooting an intermittent performance issue in a web application. Application Insights shows a high number of failed dependency calls to Azure SQL Database. The errors are SqlException with error code -2 (timeout). What is the most likely cause and recommended fix?

Trap 1: The SQL Server firewall is blocking the application IP; add a…

Firewall issues cause login failures, not timeouts.

Trap 2: The database is experiencing deadlocks; enable read committed…

Deadlocks cause error 1205, not timeout.

Trap 3: The database DTU limit is being exceeded; scale up the service tier.

DTU throttling causes different errors.

Study all Implement an instrumentation strategy common traps →

A
The application is exhausting the connection pool; increase Max Pool Size in the connection string.
Timeouts often indicate pool exhaustion.
B
The SQL Server firewall is blocking the application IP; add a firewall rule.
Why wrong: Firewall issues cause login failures, not timeouts.
C
The database is experiencing deadlocks; enable read committed snapshot isolation.
Why wrong: Deadlocks cause error 1205, not timeout.
D
The database DTU limit is being exceeded; scale up the service tier.
Why wrong: DTU throttling causes different errors.

Full breakdown with real-world context →

Question 6easymulti select

Read the full Implement an instrumentation strategy explanation →

Which TWO metrics should you monitor to evaluate the reliability of a web application according to the DORA metrics?

Trap 1: Lead Time for Changes

This measures speed, not reliability.

Trap 2: CPU Usage

Not a DORA metric.

Trap 3: Deployment Frequency

This measures velocity, not reliability.

Study all Implement an instrumentation strategy common traps →

A
Lead Time for Changes
Why wrong: This measures speed, not reliability.
B
Change Failure Rate
Measures how often deployments cause failures.
C
Mean Time to Restore (MTTR)
Measures how quickly you recover from failures.
D
CPU Usage
Why wrong: Not a DORA metric.
E
Deployment Frequency
Why wrong: This measures velocity, not reliability.

Full breakdown with real-world context →

Question 7mediummulti select

Read the full network assurance explanation →

Which THREE of the following are valid techniques to reduce the volume of telemetry data sent to Application Insights while preserving diagnostic value?

Trap 1: Aggregate telemetry into 1-minute buckets before sending

Application Insights does not accept pre-aggregated telemetry.

Trap 2: Set a maximum telemetry throughput limit on the Application…

No such limit setting exists.

Study all Implement an instrumentation strategy common traps →

A
Aggregate telemetry into 1-minute buckets before sending
Why wrong: Application Insights does not accept pre-aggregated telemetry.
B
Configure fixed-rate sampling
Keeps only a percentage of telemetry.
C
Enable adaptive sampling
Automatically adjusts sampling rate based on traffic.
D
Set a maximum telemetry throughput limit on the Application Insights instance
Why wrong: No such limit setting exists.
E
Use ITelemetryProcessor to filter out low-importance telemetry
Custom filters can drop unwanted events.

Full breakdown with real-world context →

Question 8mediummultiple choice

Read the full Implement an instrumentation strategy explanation →

You have deployed an Azure Resource Manager (ARM) template for a scheduled query rule as shown. The rule is enabled and targets an Application Insights resource. However, no alerts are firing despite HTTP 500 errors occurring. What is the most likely cause?

Exhibit

Refer to the exhibit.

```json
{
  "properties": {
    "name": "test-rule",
    "description": "Alert when error rate > 5%",
    "severity": 2,
    "enabled": true,
    "scopes": ["/subscriptions/.../resourceGroups/.../providers/microsoft.insights/components/myapp"],
    "criteria": {
      "allOf": [
        {
          "metricName": "requests/count",
          "metricNamespace": "microsoft.insights/components",
          "operator": "GreaterThan",
          "threshold": 100,
          "timeAggregation": "Total",
          "dimensions": [
            {
              "name": "request/resultCode",
              "operator": "Include",
              "values": ["500"]
            }
          ]
        }
      ]
    }
  }
}
```

Trap 1: The severity is set to 2, which suppresses the alert.

Severity 2 is valid and does not suppress alerts.

Trap 2: The metric name 'requests/count' is misspelled; it should be…

The metric name is correct.

Trap 3: The dimension filter for 'request/resultCode' includes '500' but…

Using '500' is valid; it doesn't need wildcard.

Study all Implement an instrumentation strategy common traps →

A
The severity is set to 2, which suppresses the alert.
Why wrong: Severity 2 is valid and does not suppress alerts.
B
The threshold of 100 is too high; the rule should use a percentage-based condition on error rate.
The rule should be based on failed requests percentage, not absolute count.
C
The metric name 'requests/count' is misspelled; it should be 'requests/count' (correct).
Why wrong: The metric name is correct.
D
The dimension filter for 'request/resultCode' includes '500' but should also include '5xx' wildcard.
Why wrong: Using '500' is valid; it doesn't need wildcard.

Full breakdown with real-world context →

Question 9hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

You are debugging a production issue using Application Insights Snapshot Debugger. The exhibit shows a snapshot from a NullReferenceException. The variable _dbContext is null. What is the most likely root cause?

Exhibit

Refer to the exhibit.

```
Application Insights Snapshot Debugger

Snapshot 1: 
  Thread: 1234
  Exception: NullReferenceException
  Stack:
    Contoso.Web.Pages.Index.OnGet() line 42
    Microsoft.AspNetCore.Mvc.RazorPages.Infrastructure.PageActionInvoker.InvokeHandlerMethod()
    ...
  Local variables:
    _dbContext: null (Contoso.Data.AppDbContext)
```

Trap 1: The call to the database is not awaited, causing a race condition.

NullReferenceException is due to null reference, not async.

Trap 2: The database connection string is invalid in appsettings.json.

A bad connection string would cause a different exception at runtime.

Trap 3: The OnGet method is missing a null check for _dbContext before…

Null check is a workaround, not root cause.

Study all Implement an instrumentation strategy common traps →

A
The call to the database is not awaited, causing a race condition.
Why wrong: NullReferenceException is due to null reference, not async.
B
The Index page model is not registered in the dependency injection container.
If the page model isn't registered, DI can't inject _dbContext.
C
The database connection string is invalid in appsettings.json.
Why wrong: A bad connection string would cause a different exception at runtime.
D
The OnGet method is missing a null check for _dbContext before usage.
Why wrong: Null check is a workaround, not root cause.

Full breakdown with real-world context →

Question 10hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

You are a DevOps engineer for a large e-commerce company. The company uses Azure DevOps for CI/CD and Application Insights for monitoring. The application is a .NET Core 6 microservice running on Azure Kubernetes Service (AKS) with a Redis cache and Azure SQL Database. Recently, the operations team noticed that the application's response time has degraded significantly during peak traffic hours. Application Insights shows an increase in server-side dependency call duration to Redis and SQL, but no increase in exceptions. The team suspects a connection pooling issue. You have been asked to diagnose and fix the problem. Which approach should you take first?

Trap 1: Increase the number of AKS nodes to handle peak traffic and reduce…

This treats the symptom, not the cause, and may not address connection pooling.

Trap 2: Review the application code to ensure that Redis and SQL…

While important, this is time-consuming and should follow telemetry analysis.

Trap 3: Run a load test against the application to reproduce the issue and…

Load testing may not replicate production conditions and could introduce additional load.

Study all Implement an instrumentation strategy common traps →

A
Increase the number of AKS nodes to handle peak traffic and reduce resource contention.
Why wrong: This treats the symptom, not the cause, and may not address connection pooling.
B
Review the application code to ensure that Redis and SQL connections are properly opened and closed using 'using' statements.
Why wrong: While important, this is time-consuming and should follow telemetry analysis.
C
Run a load test against the application to reproduce the issue and monitor system counters.
Why wrong: Load testing may not replicate production conditions and could introduce additional load.
D
Use Application Insights 'Dependency' performance blade to analyze call duration percentiles and identify whether the bottleneck is in Redis or SQL. Then adjust connection pool sizes accordingly.
This allows data-driven diagnosis of which dependency is causing the delay.

Full breakdown with real-world context →

Question 11mediummultiple choice

Read the full Implement an instrumentation strategy explanation →

A team uses Azure Monitor to collect metrics from their Azure Kubernetes Service (AKS) cluster. They notice that container logs are missing from Log Analytics. The cluster was created with the default settings and no custom configuration was applied. What is the most likely reason for the missing logs?

Trap 1: The diagnostic setting for the AKS cluster is not configured to…

Diagnostic settings are for control plane logs, not container logs.

Trap 2: The Log Analytics workspace is in a different region than the AKS…

Cross-region workspaces are supported for AKS.

Trap 3: The Azure Monitor agent version on the nodes is outdated.

The agent auto-updates; version is unlikely the cause.

Study all Implement an instrumentation strategy common traps →

A
The diagnostic setting for the AKS cluster is not configured to send logs to Log Analytics.
Why wrong: Diagnostic settings are for control plane logs, not container logs.
B
The Log Analytics workspace is in a different region than the AKS cluster.
Why wrong: Cross-region workspaces are supported for AKS.
C
The Data Collection Rule does not include the ContainerLogs table.
A DCR must specify which tables to collect; by default, container logs are not collected.
D
The Azure Monitor agent version on the nodes is outdated.
Why wrong: The agent auto-updates; version is unlikely the cause.

Full breakdown with real-world context →

Question 12hardmultiple choice

Read the full network assurance explanation →

A development team is implementing a distributed tracing solution for a microservices application deployed on Azure. They want to correlate requests across services using OpenTelemetry and send data to Azure Monitor. The application currently generates traces, but the traces are incomplete, showing only individual service spans without end-to-end correlation. The team has already instrumented each service with the OpenTelemetry SDK. What should the team do to ensure proper end-to-end trace correlation?

Trap 1: Enable the Application Insights auto-instrumentation agent on the…

Auto-instrumentation is for metrics, not trace context propagation.

Trap 2: Configure the OpenTelemetry SDK to use the Azure Monitor exporter…

Exporter choice does not affect context propagation.

Trap 3: Set the same service name for all services in the OpenTelemetry…

Service name is for identification, not trace correlation.

Study all Implement an instrumentation strategy common traps →

A
Implement context propagation by passing trace headers between services.
Context propagation is required to correlate spans across services.
B
Enable the Application Insights auto-instrumentation agent on the application host.
Why wrong: Auto-instrumentation is for metrics, not trace context propagation.
C
Configure the OpenTelemetry SDK to use the Azure Monitor exporter instead of the default exporter.
Why wrong: Exporter choice does not affect context propagation.
D
Set the same service name for all services in the OpenTelemetry configuration.
Why wrong: Service name is for identification, not trace correlation.

Full breakdown with real-world context →

Question 13easymulti select

Read the full Implement an instrumentation strategy explanation →

Which TWO are best practices when configuring alerts in Azure Monitor for a production application?

Trap 1: Use metric alerts only for simple threshold-based conditions.

Metric alerts support multiple conditions and dynamic thresholds.

Trap 2: Create separate alert rules for each condition to avoid complexity.

Combining conditions in one rule reduces management overhead.

Trap 3: Ensure each alert rule uses a unique action group to isolate…

Action groups can be reused across multiple rules.

Study all Implement an instrumentation strategy common traps →

A
Use metric alerts only for simple threshold-based conditions.
Why wrong: Metric alerts support multiple conditions and dynamic thresholds.
B
Use dynamic thresholds for metrics with seasonal patterns.
Dynamic thresholds automatically adjust baselines.
C
Create separate alert rules for each condition to avoid complexity.
Why wrong: Combining conditions in one rule reduces management overhead.
D
Configure action groups to send notifications and run automated actions.
Action groups are essential for alert response.
E
Ensure each alert rule uses a unique action group to isolate notifications.
Why wrong: Action groups can be reused across multiple rules.

Full breakdown with real-world context →

Question 14hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

You are reviewing a Data Collection Rule (DCR) for an Azure virtual machine. The DCR is assigned to the VM, and the Azure Monitor Agent is installed. After one hour, no performance data appears in the Log Analytics workspace. What is the most likely cause?

Exhibit

Refer to the exhibit.

```json
{
  "apiVersion": "microsoft.insights/v1",
  "location": "eastus",
  "properties": {
    "dataFlows": [
      {
        "destinations": ["la-workspace"],
        "streams": ["Microsoft-InsightsMetrics"]
      }
    ],
    "dataSources": {
      "performanceCounters": [
        {
          "name": "cpuCounter",
          "counterSpecifiers": [
            "\\Processor(_Total)\\% Processor Time"
          ],
          "samplingFrequencyInSeconds": 60
        }
      ]
    },
    "destinations": {
      "logAnalytics": [
        {
          "workspaceResourceId": "/subscriptions/sub-id/resourceGroups/rg/providers/Microsoft.OperationalInsights/workspaces/myworkspace"
        }
      ]
    }
  }
}
```

Trap 1: The workspace resource ID is incorrectly formatted.

The resource ID appears correctly formatted.

Trap 2: The counter specifier uses the wrong format for Windows performance…

The format '\\Processor(_Total)\\% Processor Time' is correct for Windows.

Trap 3: The sampling frequency of 60 seconds is too low and data is being…

60 seconds is a valid sampling interval.

Study all Implement an instrumentation strategy common traps →

A
The workspace resource ID is incorrectly formatted.
Why wrong: The resource ID appears correctly formatted.
B
The data flow streams property does not include 'Microsoft-Perf'.
Performance counters require the 'Microsoft-Perf' stream in the data flow.
C
The counter specifier uses the wrong format for Windows performance counters.
Why wrong: The format '\\Processor(_Total)\\% Processor Time' is correct for Windows.
D
The sampling frequency of 60 seconds is too low and data is being throttled.
Why wrong: 60 seconds is a valid sampling interval.

Full breakdown with real-world context →

Question 15hardmultiple choice

Read the full Implement an instrumentation strategy explanation →

A company has a multi-region application deployed on Azure App Service (Windows) across three regions: West US, East US, and West Europe. The operations team uses Azure Monitor to collect application logs and metrics. Recently, they noticed that the application in West US is experiencing high CPU usage (sustained above 90%) during peak hours, while the other regions remain below 60%. The team has set up an autoscale rule on the App Service plan to scale out when CPU exceeds 80% for 10 minutes. However, autoscale is not triggering, and the application in West US is becoming slow. The team has verified that the autoscale rule is correctly configured, the instance count is below the maximum, and there are no scale-in rules interfering. The metric data appears in Azure Monitor. You suspect that the metric alert that triggers autoscale is not firing. What is the most likely cause?

Trap 1: The autoscale rule was created less than 24 hours ago and needs a…

Autoscale rules become effective immediately; no learning period.

Trap 2: The metric collection interval for CPU is set to 30 minutes,…

Autoscale evaluates metrics every few minutes, not dependent on collection interval.

Trap 3: The autoscale rule is configured to use Log Analytics queries…

Autoscale uses platform metrics, not Log Analytics queries.

Study all Implement an instrumentation strategy common traps →

A
The autoscale rule is using the wrong metric aggregation or namespace, such as 'CpuTime' instead of 'Percentage CPU'.
Autoscale requires the correct metric and aggregation to trigger.
B
The autoscale rule was created less than 24 hours ago and needs a learning period.
Why wrong: Autoscale rules become effective immediately; no learning period.
C
The metric collection interval for CPU is set to 30 minutes, causing a delay in autoscale evaluation.
Why wrong: Autoscale evaluates metrics every few minutes, not dependent on collection interval.
D
The autoscale rule is configured to use Log Analytics queries instead of platform metrics.
Why wrong: Autoscale uses platform metrics, not Log Analytics queries.

Full breakdown with real-world context →

Question 16mediummulti select

Read the full Implement an instrumentation strategy explanation →

A company deploys a web application to Azure App Service. They want to monitor application performance and detect anomalies using Application Insights. Which two components should be configured?

Trap 1: Deploy the Azure Monitor agent on the App Service instance

Azure Monitor agent is for VMs, not App Service.

Trap 2: Create a Log Analytics workspace and configure data collection rules

Log Analytics stores data but does not directly detect anomalies.

Trap 3: Enable diagnostics settings to stream logs to Event Hubs

Streams platform logs, not application telemetry for performance anomalies.

Study all Implement an instrumentation strategy common traps →

A
Deploy the Azure Monitor agent on the App Service instance
Why wrong: Azure Monitor agent is for VMs, not App Service.
B
Configure sampling to reduce data volume
Sampling preserves representative data and reduces cost while still enabling anomaly detection.
C
Create a Log Analytics workspace and configure data collection rules
Why wrong: Log Analytics stores data but does not directly detect anomalies.
D
Enable application-side SDK for telemetry
SDK collects detailed performance and usage data needed for anomaly detection.
E
Enable diagnostics settings to stream logs to Event Hubs
Why wrong: Streams platform logs, not application telemetry for performance anomalies.

Full breakdown with real-world context →

Question 17easymulti select

Read the full Implement an instrumentation strategy explanation →

A company runs a critical microservices application on Azure Kubernetes Service (AKS). They need to implement distributed tracing across services using Application Insights. Which three actions should be performed?

Trap 1: Deploy the Application Insights agent as a DaemonSet on each AKS…

Agent collects node-level metrics, not distributed tracing.

Trap 2: Enable Azure Monitor Container Insights on the AKS cluster

Container Insights provides metrics and logs, not distributed tracing.

Study all Implement an instrumentation strategy common traps →

A
Deploy the Application Insights agent as a DaemonSet on each AKS node
Why wrong: Agent collects node-level metrics, not distributed tracing.
B
Use OpenTelemetry SDK with Application Insights exporter
OpenTelemetry provides standardized distributed tracing and can export to Application Insights.
C
Instrument each microservice with the Application Insights SDK
SDK generates telemetry for distributed tracing.
D
Set the same instrumentation key for all microservices
Same key correlates traces across services in a single Application Insights resource.
E
Enable Azure Monitor Container Insights on the AKS cluster
Why wrong: Container Insights provides metrics and logs, not distributed tracing.

Full breakdown with real-world context →

Question 18mediummultiple choice

Read the full Implement an instrumentation strategy explanation →

A company deploys a .NET Core web application to Azure App Service. The application uses Application Insights for monitoring. The operations team reports that dependency tracking is missing for calls to a third-party REST API made using HttpClient. The application is instrumented with the Application Insights SDK. Which action should be taken to enable dependency tracking for HttpClient calls?

Trap 1: Install the Application Insights Agent on the App Service instance.

The Agent is for codeless attach, not needed when SDK is used.

Trap 2: Add a reference to…

This is for custom instrumentation; SDK auto-collects HttpClient calls.

Trap 3: Use the Application Insights Status Monitor to enable dependency…

Status Monitor is for on-premises .NET Framework apps.

Study all Implement an instrumentation strategy common traps →

A
Ensure the SDK is configured using services.AddApplicationInsightsTelemetry(); in the Startup.cs file.
This enables automatic dependency tracking for HttpClient.
B
Install the Application Insights Agent on the App Service instance.
Why wrong: The Agent is for codeless attach, not needed when SDK is used.
C
Add a reference to Microsoft.ApplicationInsights.DependencyCollector and call DependencyTrackingModule.Initialize().
Why wrong: This is for custom instrumentation; SDK auto-collects HttpClient calls.
D
Use the Application Insights Status Monitor to enable dependency tracking.
Why wrong: Status Monitor is for on-premises .NET Framework apps.

Full breakdown with real-world context →

Question 19hardmulti select

Read the full Implement an instrumentation strategy explanation →

A company uses Azure Monitor and Application Insights to monitor a microservices application deployed on Azure Kubernetes Service (AKS). The development team wants to implement distributed tracing to correlate requests across services. They currently have Application Insights SDKs instrumented in each service. Which TWO configurations are required to enable end-to-end distributed tracing?

Trap 1: Enable the Live Metrics Stream feature in Application Insights.

Live Metrics Stream does not affect distributed tracing.

Trap 2: Configure adaptive sampling in the Application Insights SDK.

Sampling does not enable tracing correlation.

Trap 3: Enable Application Map in the Azure portal for each service.

Application Map is a result of tracing, not a configuration.

Study all Implement an instrumentation strategy common traps →

A
Enable the Live Metrics Stream feature in Application Insights.
Why wrong: Live Metrics Stream does not affect distributed tracing.
B
Ensure all services use the same Application Insights instrumentation key or connection string.
Same key allows correlation across services.
C
Configure adaptive sampling in the Application Insights SDK.
Why wrong: Sampling does not enable tracing correlation.
D
Ensure the SDKs are configured to propagate correlation headers (e.g., W3C Trace-Context).
Header propagation is key for distributed tracing.
E
Enable Application Map in the Azure portal for each service.
Why wrong: Application Map is a result of tracing, not a configuration.

Full breakdown with real-world context →

Question 20mediumdrag order

Read the full Implement an instrumentation strategy explanation →

Drag and drop the steps to configure Azure DevOps artifact feeds for NuGet packages into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

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What does the AZ-400 exam test about Implement an instrumentation strategy?: Implement an instrumentation strategy questions test whether you can apply the concept in context, not just recognise a definition.
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Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Implement an instrumentation strategy practice questions

What to know about Implement an instrumentation strategy

Common Implement an instrumentation strategy exam traps

Implement an instrumentation strategy questions

You are configuring Application Insights for a .NET Core web application deployed to Azure App Service. The application must capture telemetry for all HTTP requests, exceptions, and dependency calls with minimal code changes. What should you do?

Your team uses Azure DevOps for CI/CD. You need to ensure that every build publishes telemetry to Application Insights, including build duration, test pass rate, and deployment frequency. Which approach should you use?

You have an Azure DevOps pipeline that deploys a web app to Azure App Service. You want to capture deployment frequency and change failure rate as metrics in Application Insights. Which built-in analytics view should you use?

You are troubleshooting an intermittent performance issue in a web application. Application Insights shows a high number of failed dependency calls to Azure SQL Database. The errors are SqlException with error code -2 (timeout). What is the most likely cause and recommended fix?

Which TWO metrics should you monitor to evaluate the reliability of a web application according to the DORA metrics?

Which THREE of the following are valid techniques to reduce the volume of telemetry data sent to Application Insights while preserving diagnostic value?

You have deployed an Azure Resource Manager (ARM) template for a scheduled query rule as shown. The rule is enabled and targets an Application Insights resource. However, no alerts are firing despite HTTP 500 errors occurring. What is the most likely cause?

Exhibit

You are debugging a production issue using Application Insights Snapshot Debugger. The exhibit shows a snapshot from a NullReferenceException. The variable _dbContext is null. What is the most likely root cause?

Exhibit

Which TWO are best practices when configuring alerts in Azure Monitor for a production application?

You are reviewing a Data Collection Rule (DCR) for an Azure virtual machine. The DCR is assigned to the VM, and the Azure Monitor Agent is installed. After one hour, no performance data appears in the Log Analytics workspace. What is the most likely cause?

Exhibit

A company deploys a web application to Azure App Service. They want to monitor application performance and detect anomalies using Application Insights. Which two components should be configured?

A company runs a critical microservices application on Azure Kubernetes Service (AKS). They need to implement distributed tracing across services using Application Insights. Which three actions should be performed?

Drag and drop the steps to configure Azure DevOps artifact feeds for NuGet packages into the correct order.

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