AZ-204 Monitor, troubleshoot, and optimize Azure solutions Practice Test 5 — 15 Questions

Question 1

A developer needs to run a Kusto query against application request data to identify 95th percentile latency by operation. Where should the query be run?

Accepted Answer

Logs in Application Insights or the associated Log Analytics workspace. Application Insights stores application request data, including latency metrics, and supports Kusto queries via its Logs blade. The associated Log Analytics workspace also provides the same query capabilities, making it the correct location to run a Kusto query for 95th percentile latency by operation.

Answer

Microsoft Entra audit logs

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Azure Key Vault diagnostic settings

Answer

Azure Resource Graph only

Question 2

A production API needs proactive alerting for unexpected exceptions. Which two elements are required for a useful Azure Monitor alert?

Accepted Answer

A signal or metric/log query that detects the condition. A is correct because an Azure Monitor alert requires a signal (such as a metric, log query, or activity log event) to define the condition that triggers the alert. Without a signal, the alert has no basis for evaluation, making it impossible to detect unexpected exceptions proactively.

Answer

A public IP address on the app

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A manually exported CSV report

Question 3

An Azure App Service application has availability failures. The developer needs distributed tracing across requests and dependencies. What should be enabled?

Accepted Answer

Application Insights with dependency tracking. Application Insights with dependency tracking is the correct choice because it provides distributed tracing across requests and dependencies in an Azure App Service application. It automatically collects telemetry data, including end-to-end transaction details, and maps dependencies like SQL databases, HTTP calls, and Azure services, enabling developers to diagnose availability failures by correlating traces across components.

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Storage account static website logs

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Azure Policy compliance scan

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Cost Management budgets only

Question 4

Application Insights ingestion cost is rising because a high-traffic app emits large telemetry volume. The team needs statistically useful telemetry while reducing ingestion. What should be configured? The design must avoid adding custom operational scripts.

Accepted Answer

Adaptive sampling. Adaptive sampling is the correct solution because it automatically adjusts the volume of telemetry data collected from your application, ensuring that only a representative fraction of events is sent to Application Insights while preserving statistical accuracy for analysis. This reduces ingestion costs without requiring custom scripts or manual intervention, as it is a built-in feature of the Application Insights SDK that dynamically adapts based on traffic patterns.

Answer

Move the app to a larger App Service plan

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Disable all exception telemetry

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Increase log verbosity to debug

Question 5

A developer needs to run a Kusto query against application request data to identify 95th percentile latency by operation. Where should the query be run?

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

Accepted Answer

Logs in Application Insights or the associated Log Analytics workspace. Application Insights and its associated Log Analytics workspace store application request data and support Kusto Query Language (KQL) queries. Running a Kusto query against the `requests` table in the Logs workspace allows you to calculate percentile latency (e.g., using the `percentiles()` function) without custom operational scripts, as this is a built-in capability.

Answer

Microsoft Entra audit logs

Answer

Azure Key Vault diagnostic settings

Answer

Azure Resource Graph only

Question 6

A developer needs to run a Kusto query against application request data to identify 95th percentile latency by operation. Where should the query be run?

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

Accepted Answer

Logs in Application Insights or the associated Log Analytics workspace. Application Insights stores telemetry data, including request latency, in a Log Analytics workspace. Kusto queries against this data can compute percentiles (e.g., 95th) using the `percentile()` function. This is the correct location because the architecture review board prefers a managed Azure-native control, and Log Analytics is the native Azure monitoring service for running such queries.

Answer

Microsoft Entra audit logs

Answer

Azure Key Vault diagnostic settings

Answer

Azure Resource Graph only

Question 7

A production API needs proactive alerting for high telemetry cost. Which two elements are required for a useful Azure Monitor alert?

Accepted Answer

A signal or metric/log query that detects the condition. An Azure Monitor alert requires a signal—either a metric (e.g., number of API calls, or a custom metric for telemetry cost) or a log query (e.g., Application Insights traces analyzed for cost patterns)—that defines the condition to detect high telemetry cost. Without this signal, the alert has no data source to evaluate against a threshold or pattern, making proactive detection impossible. Additionally, a useful alert requires an action group to define what happens when the alert condition is met, such as sending notifications (email, SMS) or triggering automated actions (webhooks, runbooks). Without an action group, the alert would trigger but provide no practical benefit.

Answer

A public IP address on the app

Answer

A manually exported CSV report

Question 8

A high-traffic API sends approximately 80,000 telemetry events per second to Application Insights. Monthly ingestion costs are too high. The team wants to reduce data volume by roughly 80 percent while still seeing representative samples of all request types. What should the developer configure?

Accepted Answer

Enable adaptive sampling in the Application Insights SDK and set a target events-per-second limit. Adaptive sampling in the Application Insights SDK automatically adjusts the volume of telemetry sent to the service, targeting a specified rate of events per second. By setting a target that reduces the original 80,000 events/sec to roughly 20%, the SDK will intelligently sample all request types proportionally, preserving representative data while cutting costs.

Answer

Configure ingestion sampling in the Azure portal to retain 20 percent of incoming telemetry

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Increase the TelemetryClient flush interval from 30 seconds to 5 minutes to batch events

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Filter all events with HTTP status code 200 from the telemetry pipeline before sending

Question 9

The team needs to receive an email when an App Service's HTTP 5xx error rate exceeds 5 percent for more than five consecutive minutes. No custom code should be written. What combination of Azure Monitor features implements this requirement?

Accepted Answer

Create a log alert that queries the App Service diagnostic log table every 5 minutes and emails the team if the 5xx count exceeds a threshold. The explanation incorrectly states that a 5% threshold on the 'Http5xxErrors' metric can directly represent a 5% error rate. This metric is a count, and standard metric alerts do not provide a built-in mechanism to calculate its percentage relative to total requests. To achieve the required 'rate' calculation, a log alert with a Kusto Query Language (KQL) query is necessary to compute the ratio of 5xx errors to total requests over the specified time window. KQL queries within log alerts are considered configuration, not custom code, thus meeting all requirements.

Answer

Create a metric alert on the Http5xxErrors metric with a 5-percent threshold, a 5-minute evaluation window, and an action group that sends email

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Enable Application Insights availability tests and configure an alert on test failure rate

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Configure a diagnostic setting to stream logs to Azure Storage, then write a Function that reads the storage file and sends email when errors are found

Question 10

Three microservices collaborate on a single user transaction: an App Service API, an Azure Function that processes a Service Bus message, and a downstream storage service. Traces appear separately in Application Insights with no parent-child relationship. What is needed to correlate all three into a single end-to-end trace?

Accepted Answer

Install the Application Insights SDK on all three services and ensure W3C Trace Context header propagation is enabled for both HTTP calls and Service Bus messages. Distributed tracing across HTTP and asynchronous messaging requires the Application Insights SDK on each service and propagation of the W3C Trace-Context standard (traceparent and tracestate headers). This ensures that the App Service API, Azure Function, and downstream storage service share a single trace ID, enabling Application Insights to correlate all telemetry into one end-to-end transaction view.

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Use the same Application Insights instrumentation key for all three services — no additional configuration is needed

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Add a custom x-correlation-id header in each service and log it with TelemetryClient.TrackEvent

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Enable Azure Monitor cross-resource queries and write a KQL join across all three services' logs

Question 11

After deploying a new version to the staging slot and swapping to production, users report a 60-second spike in 503 errors. The application takes 45 seconds to initialize its connection pools and caches before it can serve traffic. What is the root cause, and what should the developer configure to prevent this?

Accepted Answer

Configure Application Initialization in the App Service settings so the swap waits for the warm-up path to return 200 before redirecting production traffic. The root cause is that the swap operation immediately redirects production traffic to the new slot before the application has finished its 45-second initialization (connection pools, caches). Application Initialization (warm-up) in Azure App Service can be configured to send a request to a specified path and wait for a 200 response before completing the swap, ensuring the app is ready to serve traffic. This eliminates the 503 errors by preventing the swap from routing users to an uninitialized instance.

Answer

Roll back the slot swap and investigate the new version for bugs that only appear in production

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Increase the App Service health check grace period so the load balancer waits longer after the swap

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Disable Always On for the staging slot so the slot starts fresh on every swap

Question 12

Users of a web application hosted on App Service are randomly signed out when the app is scaled out to three instances. Investigation shows that session data stored in in-process memory is not available when subsequent requests hit a different instance. What is the recommended solution?

Accepted Answer

Store session data in Azure Cache for Redis and configure all App Service instances to connect to the same Redis endpoint. When an App Service scales out to multiple instances, in-process session state is stored locally on each instance and is not shared. Azure Cache for Redis provides a centralized, in-memory data store that all instances can access, ensuring session data is available regardless of which instance handles a request. This is the recommended pattern for distributed session state in Azure.

Answer

Enable ARR affinity (sticky sessions) on the App Service to route each user's requests to the same instance

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Write session data to Azure Blob Storage as a JSON file keyed by session ID on every request

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Store session state in a Cosmos DB container with a TTL equal to the session timeout

Question 13

Arrange the steps to configure auto-scaling for an Azure App Service in the correct order.

Accepted Answer

Navigate to App Service, then scale out, then enable autoscale, then configure rules, then set limits.. First navigate to App Service, then scale out, enable autoscale, configure rules, set limits.

Answer

Navigate to App Service, then enable autoscale, then scale out, then configure rules, then set limits.

Answer

Navigate to App Service, then scale out, then configure rules, then enable autoscale, then set limits.

Answer

Navigate to App Service, then scale out, then enable autoscale, then set limits, then configure rules.

Question 14

Arrange the steps to create a CI/CD pipeline using Azure DevOps for an Azure App Service in the correct order.

Accepted Answer

Create a repository and push application code, then create a build pipeline, then configure CI trigger, then create a release pipeline, then set up approval gates.. The correct sequence for setting up a CI/CD pipeline in Azure DevOps is: First, create a repository and push your application code. Next, create a build pipeline to compile and package your application. Then, configure the CI trigger within the build pipeline to automate builds on code changes. After that, create a release pipeline to define the deployment steps for the build artifacts. Finally, set up approval gates within the release pipeline to control deployments to different environments.

Answer

Create a repository and push application code, then create a build pipeline, then create a release pipeline, then configure CI trigger, then set up approval gates.

Answer

Create a build pipeline, then create a repository and push application code, then create a release pipeline, then configure CI trigger, then set up approval gates.

Answer

Create a repository and push application code, then create a release pipeline, then create a build pipeline, then configure CI trigger, then set up approval gates.

Question 15

Match each Azure container service to its primary use case.

Accepted Answer

AKS: Orchestrates containerized applications at scale using Kubernetes.. The correct matches are: AKS → orchestration with Kubernetes; ACI → on-demand containers; ACR → image registry; Container Apps → serverless containers with autoscaling. Common confusions include swapping AKS with ACI or mixing orchestration with simple container execution.

Answer

AKS: Runs isolated containers on demand without managing underlying infrastructure.

Answer

ACI: Orchestrates containerized applications at scale using Kubernetes.