AZ-204 Monitor, troubleshoot, and optimize Azure solutions — Timed 10 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?

10 minute time limit — choose an answer to begin.

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.

Answer

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

Answer

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

Answer

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'.

Answer

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.

Answer

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.

Answer

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.

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 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.

Answer

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.

Answer

Allows all traffic

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Blocks traffic from 192.168.1.0/24 and 10.0.0.0/8

Answer

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.

Answer

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

Answer

Network bytes sent and received