AZ-204 Monitor, troubleshoot, and optimize Azure solutions — 50 Questions

Question 1

You are monitoring an e-commerce application with Application Insights. You need to analyze all exceptions that occurred in the last 24 hours, grouped by the exception type. You also need to include the URL where each exception was triggered and the number of times each type occurred. Which Log Analytics Kusto query should you use?

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

Accepted Answer

exceptions | where timestamp > ago(24h) | join kind=inner requests on operation_Id | extend exceptionType = tostring(innermostType) | summarize Count=count() by exceptionType, url. It uses the `exceptions` table to filter exceptions from the last 24 hours, joins with the `requests` table on `operation_Id` to correlate each exception with the request URL, and then summarizes the count by exception type (extracted from `innermostType`) and URL. This meets all requirements: grouping by exception type, including the URL, and counting occurrences.

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exceptions | where timestamp > ago(24h) | extend exceptionType = tostring(customDimensions.['ExceptionType']) | summarize Count=count() by exceptionType, url = tostring(customDimensions.['Url'])

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requests | where timestamp > ago(24h) and success == false | extend exceptionType = tostring(resultCode) | summarize Count=count() by exceptionType, url

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exceptions | where timestamp > ago(24h) | extend exceptionType = tostring(innermostType) | summarize Count=count() by exceptionType

Question 2

You manage a web application on Azure App Service. You need to monitor its availability from multiple geographic locations, checking that the homepage loads and returns HTTP 200 within 5 seconds. You want an alert if any location fails. Which type of Application Insights test should you create?

Accepted Answer

Standard test. A Standard test (also known as a URL ping test) is the correct choice because it is the simplest availability test in Application Insights, designed to check that a single URL returns an HTTP 200 response within a specified timeout (here, 5 seconds). It can be configured to run from multiple geographic locations, and you can set an alert to fire if any location reports a failure, meeting the requirement exactly. In the Azure portal, this test type is explicitly named 'Standard test'.

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Availability test (URL ping test)

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Multi-step web test

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Custom metric test

Question 3

A production API needs proactive alerting for failed dependency calls. Which two elements are required for a useful Azure Monitor alert?

Accepted Answer

A signal or metric/log query that detects the condition. Azure Monitor alerts require a signal—either a metric, log query, or activity log event—to define the condition that triggers the alert. For failed dependency calls, you would use a log query (e.g., from Application Insights) or a custom metric to detect when the dependency failure rate exceeds a threshold. Without a signal, the alert has no basis to evaluate or fire.

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A manually exported CSV report

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A public IP address on the app

Question 4

An Azure App Service application has memory pressure. 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 enables distributed tracing by automatically collecting telemetry across HTTP requests, database calls, and external service dependencies. This allows developers to correlate end-to-end transactions and identify the root cause of memory pressure, such as a specific dependency causing excessive resource consumption.

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Azure Policy compliance scan

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Cost Management budgets only

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Storage account static website logs

Question 5

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?

Accepted Answer

Adaptive sampling. Adaptive sampling in Application Insights automatically reduces the volume of telemetry data sent from high-traffic apps by intelligently selecting a representative subset of events. This preserves statistical utility for analysis while significantly lowering ingestion costs, making it the ideal solution for the described scenario.

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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 6

You have an App Service app that uses Azure SQL Database. Users report that some queries are slow. You need to monitor query performance and receive alerts when the average query duration exceeds 1 second. Which two Azure services should you use?

Accepted Answer

Azure Monitor Alerts. Application Insights (D) provides built-in SQL dependency tracking for your App Service app, enabling you to monitor individual query durations and collect metrics like average query duration. Azure Monitor Alerts (B) is then used to configure and receive alerts based on these metrics collected by Application Insights. When the average query duration (from Application Insights) exceeds 1 second, Azure Monitor Alerts will trigger, fulfilling both the monitoring and alerting requirements using two distinct Azure services.

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Azure Advisor

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Azure Monitor Logs

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Azure SQL Analytics

Question 7

You have an Azure Function app that processes messages from a Service Bus queue. Under high load, some messages are not processed within the expected time. You need to identify whether the function is throttling due to high CPU or due to a downstream dependency. Which Application Insights feature should you use?

Accepted Answer

Application Map. Application Map is the correct choice because it provides a visual representation of the dependencies and telemetry flow across your distributed application. By examining the Application Map, you can see the health and performance of downstream dependencies (e.g., databases, external APIs) and correlate any slowdowns or failures with the function's processing time, helping you determine if the bottleneck is due to a dependency rather than CPU throttling.

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Live Metrics Stream

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Application Insights Profiler

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Search

Question 8

You have an Azure Front Door Premium instance with a Web Application Firewall (WAF) policy. The exhibit shows a custom rule. What is the effect of this rule?

Accepted Answer

Blocks traffic that does NOT originate from 192.168.1.0/24 or 10.0.0.0/8. The rule blocks requests that do NOT originate from the specified IP ranges (negateCondition: true). Option A is wrong because it allows all traffic. Option B is wrong because it blocks only those IPs. Option D states that traffic from the specified IPs is allowed. While this is the net outcome, the rule itself is configured to *block* traffic that *does not* match the specified IPs (due to 'action: Block' and 'negateCondition: true'). Option C directly describes this blocking mechanism.

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Allows all traffic

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Blocks traffic from 192.168.1.0/24 and 10.0.0.0/8

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Allows traffic from 192.168.1.0/24 and 10.0.0.0/8

Question 9

You have an Azure Policy as shown in the exhibit. The roleDefinitionId corresponds to the 'Contributor' role. What does this policy do?

Accepted Answer

Denies Contributors from creating or modifying virtual machines. None of the listed options is correct. Azure Policy cannot use a 'roleDefinitionId' condition to deny virtual machine operations based on the role of the user initiating the request; there is no alias for the initiator's role assignment on the target resource. A policy that targets Microsoft.Authorization/roleAssignments can govern role assignment creation, not VM creation/modification. To prevent Contributors from creating or modifying VMs, change the assigned role or use a custom role with fewer permissions.

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Allows Contributors to create virtual machines

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Audits when Contributors create virtual machines

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Deploys a virtual machine when a Contributor action occurs

Question 10

Which THREE metrics should you monitor to determine if an Azure Kubernetes Service (AKS) cluster is running optimally? (Choose three.)

Accepted Answer

Node CPU usage percentage. Node CPU usage percentage (A) is correct because sustained high CPU utilization on AKS agent nodes indicates resource contention that can throttle pods and degrade scheduling, making it a core indicator of cluster health. Disk I/O throughput (D) is correct because AKS nodes rely on disk performance for container image pulls, ephemeral storage, and etcd-backed operations; poor throughput signals I/O bottlenecks that hurt overall cluster performance. Pod memory usage percentage (E) is correct because tracking memory consumption per pod helps detect memory pressure, OOMKills, and inefficient resource requests/limits, which directly affect whether workloads run optimally. API server request latency (B) is not marked correct because, while useful for control-plane health, the question targets cluster runtime optimization metrics rather than control-plane responsiveness. Network bytes sent and received (C) is not marked correct because raw byte counters alone lack the context (e.g., saturation, drops, latency) needed to judge whether the cluster is running optimally.

Answer

API server request latency

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Network bytes sent and received

Question 11

Which THREE actions should you take to optimize cost for an Azure Functions app that processes messages from a queue?

Accepted Answer

Use Consumption plan instead of Premium. The three actions that optimize cost are: A) Consumption plan charges per execution and scales to zero when idle, minimizing cost for variable workloads. B) Batch processing reduces the number of function executions by processing multiple messages in a single invocation, lowering total execution count. C) If a Premium plan is chosen, configuring auto-scale to reduce instances during off-peak periods minimizes costs by avoiding payment for *unnecessary* fixed capacity, though a base cost for minimum instances still applies. Options D (Always On) keeps the app running continuously, increasing cost. Option E (increased timeout) does not reduce cost; it may increase costs by allowing longer running executions.

Answer

Enable Always On to keep the app warm

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Increase function timeout to reduce retries

Question 12

You deploy a microservices application to Azure Kubernetes Service (AKS). The application uses Azure Cache for Redis to store session state. Users report that they are frequently logged out. You suspect that the session data is being evicted from the cache. Which configuration change should you make to reduce evictions?

Accepted Answer

Increase the maxmemory-reserved setting or change eviction policy. Increasing the `maxmemory-reserved` setting allocates more memory exclusively for non-cache operations (like replication buffers), reducing the chance that session data is evicted under memory pressure. Alternatively, changing the eviction policy to `allkeys-lru` or `volatile-lru` can prioritize keeping recently used keys, which helps retain active session state. Both adjustments directly address the symptom of frequent evictions causing session loss.

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Enable clustering for the cache

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Enable data persistence using RDB or AOF

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Upgrade to a higher-tier Azure Cache for Redis

Question 13

You are troubleshooting a slow web application hosted on Azure App Service. The application uses Azure SQL Database. You suspect that the database queries are taking too long. Which Azure Monitor feature should you use to identify slow queries?

Accepted Answer

Azure SQL Insights. Azure SQL Insights (part of Azure Monitor) is the correct choice because it provides dedicated monitoring for Azure SQL Database. It uses Log Analytics workspaces and offers prebuilt dashboards showing query performance, wait statistics, and resource utilization to identify slow-running or resource-intensive queries.

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Azure Activity Log

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Azure Advisor

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Azure Monitor Metrics

Question 14

Which THREE features of Azure Monitor can help you diagnose a performance issue in an Azure Virtual Machine?

Accepted Answer

Log Analytics workspace queries. Option A (Log Analytics workspace queries) is correct because Azure Monitor stores VM telemetry such as performance counters and event logs in a Log Analytics workspace, where KQL queries can correlate CPU, memory, disk, and network data to pinpoint the cause of a performance problem. Option B (VM Insights) is correct because it is an Azure Monitor feature that discovers VM processes and dependencies and surfaces performance trends through curated workbooks and maps, directly helping diagnose VM performance issues. Option E (Diagnostic settings to send performance counters to Log Analytics) is correct because the Azure Monitor agent's diagnostic settings must route guest performance counters (for example, \Processor(_Total)\% Processor Time) to a Log Analytics workspace before those metrics can be queried for diagnosis. Option C (Azure Network Watcher) is not correct here because it is a separate network diagnostic service (NSG flow logs, connection troubleshoot, IP flow verify) rather than an Azure Monitor feature for general VM performance diagnosis. Option D (Azure Backup reports) is not correct because it reports on backup job health and compliance, not on live VM performance metrics.

Answer

Azure Network Watcher

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Azure Backup reports

Question 15

You are a developer for a logistics company that runs an Azure Functions app on the Consumption plan. The app processes telemetry from delivery vehicles and writes to Azure Blob Storage. You need to be notified when the function's error rate exceeds 5% over a 15-minute window. You want to minimize configuration effort and cost. What should you do?

Accepted Answer

Create a metric alert rule in Azure Monitor on the Function App's 'Function Execution Count' metric with a dimension filter for status 'Failed'.. Azure Monitor metric alerts on the Function App's 'Function Execution Count' metric with a 'Status' dimension filter for Failed executions provide a native, low-cost way to alert on error rate. They require no custom code or additional services, satisfying the requirement to minimize configuration effort and cost. Other options introduce extra components or costs.

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Configure Application Insights continuous export to a storage account and build an Azure Logic App to parse the blobs and send an email when errors exceed 5%.

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Enable diagnostic settings to send Function App logs to a Log Analytics workspace and create a scheduled query alert that counts failed executions in the last 15 minutes.

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Use the Azure Functions Core Tools to run 'func azure functionapp logstream' and set up a local script to monitor the stream and send notifications.

Question 16

You are developing a .NET application that uses the Azure.Monitor.OpenTelemetry.AspNetCore library to send telemetry to Application Insights. You need to ensure that custom trace spans include the correlation ID from an incoming HTTP header 'X-Correlation-ID' so that you can filter traces by this ID in the Application Insights portal. What should you do?

Accepted Answer

Add the correlation ID as an attribute to the Activity using 'activity.SetTag("correlationId", correlationId)' and ensure the OpenTelemetry exporter is configured to include it as a custom dimension.. The Azure Monitor OpenTelemetry exporter automatically maps Activity tags (attributes) to Application Insights customDimensions. Therefore, adding the correlation ID as a tag on the Activity ensures it becomes a filterable custom property. Other approaches either rely on the classic SDK or use mechanisms not intended for telemetry enrichment.

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Add the correlation ID as a custom property on the Activity using 'activity.SetTag("correlationId", correlationId)' and configure the Application Insights SDK to map it.

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Implement an ITelemetryInitializer that reads the HTTP header and adds it to the telemetry item's Properties collection.

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Use the 'Activity.Current.AddBaggage("correlationId", correlationId)' method to add the correlation ID as baggage, which is automatically propagated and stored as a custom dimension.

Question 17

You are developing a .NET 6 console application that uses the Azure.Monitor.Query library to query Application Insights logs. You need to authenticate the application to Azure Monitor using the Azure Identity library. The application will run as an Azure AD service principal. You want to follow the principle of least privilege. Which two actions should you perform? (Choose two.)

Accepted Answer

Register the application in Azure AD and create a client secret for authentication.. To authenticate a service principal to Azure Monitor, you must first register an application in Microsoft Entra ID and create a credential (client secret or certificate). Then, you assign the least-privilege role 'Monitoring Reader' at the Application Insights resource scope. This allows the application to query logs while adhering to least privilege. Other roles are either too broad or not scoped correctly.

Answer

Use the 'DefaultAzureCredential' class from Azure.Identity to authenticate the Azure Monitor Query client.

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Assign the 'Contributor' role to the service principal at the subscription scope.

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Assign the 'Log Analytics Reader' role to the service principal at the Log Analytics workspace scope.

Question 18

An e-commerce application emits a high volume of telemetry data to Azure Application Insights. You need to reduce the cost of data ingestion while preserving statistical accuracy for performance metrics. Which sampling technique should you use?

Accepted Answer

Adaptive sampling. Adaptive sampling is the correct choice because it automatically adjusts the sampling rate based on the volume of telemetry data, ensuring that during low-traffic periods all data is retained for statistical accuracy, while during high-traffic periods it reduces the rate to control costs. This technique is specifically designed for high-volume scenarios like e-commerce telemetry, where preserving statistical accuracy for performance metrics (e.g., request durations, failure rates) is critical, and it avoids the manual tuning required by fixed-rate sampling.

Answer

Fixed-rate sampling with a 1% rate

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Ingestion sampling

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Head-based sampling

Question 19

You need to monitor the real-time CPU utilization of an Azure virtual machine. Which Azure Monitor feature is designed for this purpose?

Accepted Answer

Metrics. Azure Monitor Metrics is the correct feature because it collects and stores numeric time-series data from Azure resources, including CPU utilization, at near-real-time intervals (typically every 1 minute for Azure VMs). Metrics are lightweight, low-latency, and designed for real-time monitoring and alerting, making them ideal for tracking CPU usage without the overhead of log ingestion.

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Logs

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Alerts

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Workbooks

Question 20

You have an Azure App Service web app that experiences intermittent slowness. You enable Application Insights and notice that the "Failed Requests" metric is low, but "Server Response Time" is high for a subset of requests. You want to identify the specific code path causing the delay. Which feature should you use?

Accepted Answer

Profiler.. C is correct because the Application Insights Profiler captures detailed call stacks and execution timing for slow requests, allowing you to pinpoint the exact code path causing high server response time. Unlike other features, Profiler is specifically designed for performance troubleshooting by tracing request execution at the code level.

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Live Metrics.

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Snapshot Debugger.

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Availability tests.