AZ-400 Implement an instrumentation strategy Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The data flow streams property does not include 'Microsoft-Perf'.. The most likely cause is that the data flow streams property in the DCR does not include 'Microsoft-Perf'. The Azure Monitor Agent uses DCRs to define which data to collect and where to send it. Without the 'Microsoft-Perf' stream specified in the dataFlows section, performance counters are not collected, even if the counter specifiers are correctly defined. The workspace resource ID and counter format may be correct, but the missing stream prevents any performance data from being ingested.

Answer

The workspace resource ID is incorrectly formatted.

Answer

The counter specifier uses the wrong format for Windows performance counters.

Answer

The sampling frequency of 60 seconds is too low and data is being throttled.

Question 2

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?

Accepted Answer

The autoscale rule is using the wrong metric aggregation or namespace, such as 'CpuTime' instead of 'Percentage CPU'.. The autoscale rule must use the correct metric name and aggregation to evaluate scaling conditions. If the rule is configured with 'CpuTime' instead of 'Percentage CPU', it will not match the actual CPU utilization metric emitted by the Azure App Service plan. 'Percentage CPU' is the standard platform metric for CPU load, while 'CpuTime' measures total CPU time consumed, which does not trigger the same threshold logic. Since the team verified the rule is correctly configured but autoscale is not firing, the most likely cause is a mismatch in the metric name or namespace.

Answer

The autoscale rule was created less than 24 hours ago and needs a learning period.

Answer

The metric collection interval for CPU is set to 30 minutes, causing a delay in autoscale evaluation.

Answer

The autoscale rule is configured to use Log Analytics queries instead of platform metrics.

Question 3

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?

Accepted Answer

Configure sampling to reduce data volume. Sampling in Application Insights reduces the volume of telemetry data while preserving statistically correct analysis, which is essential for managing cost and performance in high-traffic applications. Option D is correct because the application-side SDK (e.g., Application Insights SDK for .NET, Java, or Node.js) is required to instrument the code and collect detailed telemetry such as requests, dependencies, and exceptions for performance monitoring and anomaly detection.

Answer

Deploy the Azure Monitor agent on the App Service instance

Answer

Create a Log Analytics workspace and configure data collection rules

Answer

Enable diagnostics settings to stream logs to Event Hubs

Question 4

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?

Accepted Answer

Ensure the SDK is configured using services.AddApplicationInsightsTelemetry(); in the Startup.cs file.. `services.AddApplicationInsightsTelemetry()` in `Startup.cs` automatically registers the `DependencyTrackingModule` for .NET Core applications. This module collects dependency telemetry for `HttpClient` calls made via `IHttpClientFactory` or typed `HttpClient` instances, provided the SDK is properly configured. Since the application already uses the Application Insights SDK, this single line enables automatic dependency tracking without additional packages or agents.

Answer

Install the Application Insights Agent on the App Service instance.

Answer

Add a reference to Microsoft.ApplicationInsights.DependencyCollector and call DependencyTrackingModule.Initialize().

Answer

Use the Application Insights Status Monitor to enable dependency tracking.

Question 5

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?

Accepted Answer

Ensure all services use the same Application Insights instrumentation key or connection string.. All services must share the same Application Insights instrumentation key or connection string to ensure that telemetry from different microservices is correlated into a single application map and trace. Without a common instrumentation key, the distributed trace data would be siloed across separate Application Insights resources, preventing end-to-end correlation.

Answer

Enable the Live Metrics Stream feature in Application Insights.

Answer

Configure adaptive sampling in the Application Insights SDK.

Answer

Enable Application Map in the Azure portal for each service.

Question 6

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

Accepted Answer

Step 1: Create a new feed, Step 2: Configure upstream sources, Step 3: Set feed permissions, Step 4: Publish NuGet packages, Step 5: Consume packages from feed. Feed setup starts with creation, upstream sources, permissions, publishing, and consumption.

Answer

Step 1: Create a new feed, Step 2: Set feed permissions, Step 3: Configure upstream sources, Step 4: Publish NuGet packages, Step 5: Consume packages from feed

Answer

Step 1: Create a new feed, Step 2: Publish NuGet packages, Step 3: Configure upstream sources, Step 4: Set feed permissions, Step 5: Consume packages from feed

Answer

Step 1: Create a new feed, Step 2: Configure upstream sources, Step 3: Publish NuGet packages, Step 4: Set feed permissions, Step 5: Consume packages from feed

Question 7

Match each Azure DevOps security concept to its purpose.

Accepted Answer

Azure Active Directory: Used for identity and access management, including authentication to Azure DevOps.. Microsoft Entra ID manages user identities; PATs authenticate scripts; Service Principals authenticate automated services; Security Groups manage user permissions. Common confusions include mixing the roles of Microsoft Entra ID and Service Principals, or PATs with Security Groups.

Answer

Azure Active Directory: Used to authenticate automated pipelines to Azure resources.

Answer

Personal Access Token (PAT): Used to manage permissions for groups of users.

Question 8

Your team uses Azure Pipelines to deploy a Node.js application. Recently, deployments have been failing intermittently due to a missing npm package. The pipeline runs successfully on the local agent but fails on the hosted agent. Which instrumentation strategy should you implement to identify the root cause?

Accepted Answer

Enable verbose logging for the npm install task and compare the output between pipeline runs.. Verbose logging on the npm install task surfaces the exact package resolution, registry URL, and dependency tree being used on the hosted agent, which is where the failure occurs. Comparing that output against the successful local run reveals differences such as a missing package-lock.json, a different registry, or a case-sensitivity issue on the Linux hosted agent. This is a diagnostic instrumentation step, which is what the question asks for.

Answer

Replace the hosted agent with a self-hosted agent.

Answer

Configure Application Insights for the Node.js app to monitor runtime errors.

Answer

Add a pipeline cache task for npm packages to ensure consistent restore.

Question 9

You are designing an instrumentation strategy for a microservices application deployed to Azure Kubernetes Service (AKS) using Azure Pipelines. The application emits custom metrics using OpenTelemetry. You need to ensure that all pipeline-related events (build, release, and test results) are correlated with application telemetry to enable end-to-end traceability. What should you do?

Accepted Answer

Set a unique Correlation ID in the pipeline variables and pass it to the application's OpenTelemetry instrumentation as a span attribute.. To correlate pipeline events with application telemetry, you must propagate a shared identifier from the pipeline into the application's telemetry. Setting a unique Correlation ID in pipeline variables and passing it as an OpenTelemetry span attribute (D) lets Application Insights and pipeline logs join on that ID, enabling true end-to-end traceability.

Answer

Configure Application Insights to ingest pipeline telemetry via a custom exporter.

Answer

Store pipeline logs in an Azure Log Analytics workspace and query them together with application metrics.

Answer

Use Azure Pipelines' Checks feature to enforce deployment gates based on application metrics.

Question 10

Your team uses GitHub Actions for CI/CD. You need to collect and analyze build and deployment logs centrally to identify recurring failures. Which service should you use to ingest and query these logs?

Accepted Answer

Azure Log Analytics. Azure Log Analytics is the service designed to ingest, store, and query large volumes of log data using Kusto Query Language (KQL), making it ideal for centralizing GitHub Actions build and deployment logs and identifying recurring failures. You can create a Log Analytics workspace, send logs via the Logs Ingestion API or diagnostic settings, and run KQL queries to spot patterns. This directly satisfies the requirement to collect and analyze logs centrally.

Answer

Azure Monitor Alerts

Answer

GitHub Insights

Answer

Application Insights

Question 11

Your organization uses Azure Pipelines with multiple release stages. You need to instrument the pipeline to capture the duration of each stage and the number of failed tasks. Which TWO approaches should you use?

Accepted Answer

Use the Azure DevOps REST API to retrieve pipeline run statistics after each run.. Options D and E are correct. The goal is to capture stage duration and failed task counts. Option D uses the Azure DevOps REST API to programmatically retrieve pipeline run statistics, which includes stage-level durations and failure information. Option E adds a script task at the end of each stage that logs the stage's duration and task results to a custom log file, providing direct instrumentation. Option A is incorrect because pipeline notifications only alert on failures, they don't capture or log durations. Option B is incorrect because 'runOnce' is a deployment strategy that ensures sequential stages, not an instrumentation method. Option C is incorrect because the 'Summary' tab in the pipeline run provides a high-level overview, not detailed per-stage duration data that can be programmatically captured.

Answer

Configure pipeline notifications for failed tasks.

Answer

Implement a 'runOnce' deployment strategy to ensure sequential stages.

Answer

Enable the 'Summary' tab in the pipeline run to view stage durations.

Question 12

You are building a comprehensive instrumentation strategy for a large-scale Azure DevOps environment. You need to ensure that all pipeline events (build, release, test) are automatically tracked and correlated with application performance data. Which THREE components should you include?

Accepted Answer

A shared Correlation ID that flows from pipeline to application.. Options A, C, and E are correct. A shared Correlation ID (A) flows from pipeline to application, enabling end-to-end trace correlation. Custom pipeline tasks (C) send telemetry (e.g., build/release events) to Application Insights. The OpenTelemetry SDK (E) emits trace context from the application, which can include the correlation ID. Option B is wrong because Azure Monitor Workbooks are visualization tools, not data collection components. Option D is wrong because Azure Boards is for work item tracking, not pipeline event capture.

Answer

Azure Monitor Workbooks to visualize pipeline data.

Answer

Azure Boards to track pipeline run metadata.

Question 13

Refer to the exhibit. You deploy this ARM template to create a Log Analytics workspace and a saved search. After deployment, you notice that the saved search returns no results even though there are failed pipeline runs. What is the most likely reason?

Accepted Answer

The custom table 'AzureDevOpsPipelineEvents_CL' does not exist in the workspace.. The saved search queries the custom table 'AzureDevOpsPipelineEvents_CL', which is created by the Azure DevOps connector or a custom data ingestion pipeline. If that table has never been created in the target Log Analytics workspace, the saved search KQL query will return zero rows even when failed pipeline runs exist elsewhere. The ARM template only defines the workspace and the saved search — it does not create the underlying custom table or wire up the data source.

Answer

The savedSearch API version is not supported.

Answer

The Log Analytics workspace API version is incorrect.

Answer

The category 'Azure Pipelines' is misspelled.

Question 14

You need to monitor the health of your Azure Pipeline agents. Which Azure DevOps Analytics view should you use to identify agents that have not reported in the last hour?

Accepted Answer

Agent Health dashboard. The Agent Health dashboard in Azure DevOps provides real-time status of pipeline agents, including their last communication timestamp. By filtering for agents that have not reported in the last hour, you can identify unhealthy or disconnected agents. This dashboard directly surfaces agent connectivity and health metrics. Note that this is a dashboard, not an Analytics view like Release Analytics, Test Analytics, or Pipeline Run Analytics.

Answer

Release Analytics

Answer

Test Analytics

Answer

Pipeline Run Analytics

Question 15

You are deploying a Java application to Azure App Service using Azure Pipelines. You want to automatically capture JVM metrics (heap usage, garbage collection) and correlate them with deployment events. What should you do?

Accepted Answer

Enable Application Insights Java agent in the App Service.. To automatically capture JVM metrics (heap usage, garbage collection) and correlate them with deployment events, you should enable the Application Insights Java agent in the App Service. The Java agent auto-instruments the JVM, collecting detailed metrics and telemetry without code changes. Application Insights integrates with Azure Pipelines, allowing deployment annotations to be correlated with telemetry.

Answer

Use Azure Monitor diagnostic settings for the App Service.

Answer

Attach a profiler from Visual Studio to the App Service process.

Answer

Configure App Service diagnostic logs to capture stdout/stderr.