AZ-204 Practice Questions 2 — 50 Questions

Question 1

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 is the correct solution because it automatically adjusts the volume of telemetry data sent to Application Insights, retaining only a representative subset that preserves statistical accuracy for analysis. This reduces ingestion costs while ensuring the sampled data remains statistically useful for detecting trends and anomalies in high-traffic applications.

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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 2

You are monitoring an Azure web application with Application Insights. You need to identify the top 5 slowest API endpoints over the last 7 days. The results should show the endpoint URL, average response time, and request count. Which feature or query should you use?

Accepted Answer

Use the 'Performance' blade under 'Investigate' in the Application Insights resource.. Option B is correct because the 'Performance' blade in Application Insights provides a pre-built, optimized view that automatically aggregates request data by endpoint URL, displaying average response time and request count. It allows you to sort by average duration to quickly identify the top 5 slowest API endpoints over the last 7 days without writing any custom query.

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Use Log Analytics and run a query on the 'requests' table to aggregate by URL and sort by avg(duration).

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Use the 'Application Map' feature to visualize dependencies and endpoints.

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Configure Smart Detection to automatically identify slow API endpoints.

Question 3

You are monitoring an Azure Web App using Application Insights. You need to track the duration and status code of an external API call made by the app. Which Application Insights feature should you use?

Accepted Answer

Dependency tracking feature. Dependency tracking in Application Insights is specifically designed to monitor calls made by your application to external services, such as APIs, databases, or HTTP endpoints. It automatically captures the duration, success/failure status, and response code of outbound HTTP requests, making it the correct choice for tracking an external API call's duration and status code.

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Built-in request telemetry (server-side requests)

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Custom events (TrackEvent)

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Page view tracking

Question 4

You develop a C# application that stores sensitive documents in Azure Blob Storage. You need to generate a time-limited shared access signature (SAS) that allows a client to only read and list blobs in a specific container. The SAS must be valid for exactly 1 hour from the current time. Which code snippet correctly creates the SAS? (Assume BlobServiceClient and BlobContainerClient are properly initialized.)

Accepted Answer

var sasBuilder = new BlobSasBuilder { BlobContainerName = containerName, Resource = "c", StartsOn = DateTimeOffset.UtcNow, ExpiresOn = DateTimeOffset.UtcNow.AddHours(1), Permissions = BlobContainerSasPermissions.Read | BlobContainerSasPermissions.List }; Uri sasUri = containerClient.GenerateSasUri(sasBuilder);. Option A is correct because it sets the `Resource` property to "c" for container-level SAS, uses `StartsOn` as the current UTC time, `ExpiresOn` exactly 1 hour later, and specifies only `Read` and `List` permissions via the `BlobContainerSasPermissions` enum. This combination generates a time-limited SAS URI that allows a client to read and list blobs within the specified container for exactly one hour.

Answer

var sasBuilder = new BlobSasBuilder { BlobContainerName = containerName, Resource = "b", StartsOn = DateTimeOffset.UtcNow, ExpiresOn = DateTimeOffset.UtcNow.AddHours(1), Permissions = BlobContainerSasPermissions.Read | BlobContainerSasPermissions.List }; Uri sasUri = containerClient.GenerateSasUri(sasBuilder);

Answer

var sasBuilder = new BlobSasBuilder { BlobContainerName = containerName, Resource = "c", StartsOn = DateTimeOffset.UtcNow, ExpiresOn = DateTimeOffset.UtcNow.AddHours(1), Permissions = BlobContainerSasPermissions.All }; Uri sasUri = containerClient.GenerateSasUri(sasBuilder);

Answer

var sasBuilder = new BlobSasBuilder { BlobContainerName = containerName, Resource = "c", StartsOn = DateTimeOffset.UtcNow.AddDays(-1), ExpiresOn = DateTimeOffset.UtcNow.AddHours(1), Permissions = BlobContainerSasPermissions.Read | BlobContainerSasPermissions.List }; Uri sasUri = containerClient.GenerateSasUri(sasBuilder);

Question 5

You store application logs in Azure Blob Storage. The logs are accessed frequently for the first 7 days, then rarely. After 30 days, they must be deleted to minimize cost. Which approach should you use?

Accepted Answer

Configure a lifecycle management policy to tier to cool after 7 days and delete after 30 days. Option C is correct because Azure Blob Storage lifecycle management policies allow you to automatically transition blobs to a cooler tier (cool) after a specified number of days and then delete them after another period, all without manual intervention or additional services. This directly meets the requirement of frequent access for 7 days, rare access afterward, and deletion at 30 days, minimizing cost by leveraging tiered storage and automated rules.

Answer

Manually move blobs to cool tier after 7 days and delete after 30 days using a script

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Use blob snapshots and delete snapshots after 30 days

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Use Azure Data Factory to copy old logs to archive storage and delete original

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? The design must avoid adding custom operational scripts.

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.

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Microsoft Entra audit logs

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Azure Key Vault diagnostic settings

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Azure Resource Graph only

Question 7

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

Answer

Disable all exception telemetry

Answer

Increase log verbosity to debug

Question 8

An Azure App Service application has slow API requests. 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, enabling developers to correlate end-to-end transactions in a microservices or multi-component application. It automatically collects telemetry for HTTP calls, database queries, and other external service calls, which is essential for diagnosing slow API requests in an Azure App Service environment.

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Azure Policy compliance scan

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Storage account static website logs

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Cost Management budgets only

Question 9

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 10

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 architecture review board prefers a managed AWS-native control.

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

Answer

Disable all exception telemetry

Answer

Increase log verbosity to debug

Question 11

You are debugging a performance issue in a live web application monitored by Application Insights. You need to see real-time metrics such as request rate, response times, and any exceptions as they occur, without waiting for the usual telemetry pipeline. Which Application Insights feature should you use?

Accepted Answer

Live Metrics Stream. Live Metrics Stream (option B) is the correct feature because it provides real-time, low-latency telemetry directly from the Application Insights SDK, bypassing the usual ingestion pipeline. This allows you to monitor request rate, response times, and exceptions as they occur, which is exactly what the scenario requires for debugging a live performance issue without waiting for data to be processed.

Answer

Application Insights Analytics (Log Analytics)

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Metrics Explorer

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Application Insights Profiler

Question 12

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.

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A public IP address on the app

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A manually exported CSV report

Question 13

You are using Application Insights to monitor a web application. You notice that a specific request is slow. You want to see the complete end-to-end transaction details, including all dependency calls and exceptions for that single request. Which feature should you use?

Accepted Answer

Transaction Search (End-to-End Transaction Details). Transaction Search (End-to-End Transaction Details) is the correct feature because it allows you to view the complete trace of a single request, including all dependency calls (e.g., SQL, HTTP, Azure services), exceptions, and logs associated with that specific operation. This is achieved by correlating telemetry using the operation_Id field, which groups all telemetry items from the same request into a single end-to-end view. Other features like Metrics Explorer or Application Map provide aggregated or topological views, not per-request drill-down.

Answer

Metrics Explorer

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Application Map

Answer

Live Metrics Stream

Question 14

You are developing a web API that must authenticate requests using Microsoft Entra ID (Microsoft Entra ID) and OAuth 2.0 bearer tokens. You want to validate the token in your API code. Which library should you use?

Accepted Answer

Microsoft.Identity.Web. Microsoft.Identity.Web is the recommended library for integrating ASP.NET Core web APIs with Microsoft Entra ID. It provides built-in token validation, policy enforcement, and handles the OAuth 2.0 bearer token flow, including JWT validation, issuer signing keys, and audience checks, without requiring manual configuration of middleware.

Answer

Microsoft Authentication Library (MSAL)

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ADAL.NET

Answer

Azure.Identity

Question 15

You are using Application Insights to monitor a web application. The business team wants to track how many users click a specific button on the page. You need to send custom telemetry data from the client-side JavaScript. Which Application Insights JavaScript SDK method should you call?

Accepted Answer

appInsights.trackEvent. The correct method is `trackEvent` because it is specifically designed for capturing user interactions, such as button clicks, as custom events in Application Insights. Unlike other methods, `trackEvent` allows you to attach custom properties and measurements, making it ideal for business metrics like click tracking. This method sends the data as a custom event telemetry item, which can be analyzed in the Azure portal under 'Events'.

Answer

appInsights.trackTrace

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appInsights.trackPageView

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appInsights.trackException

Question 16

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?

Accepted Answer

exceptions | where timestamp > ago(24h) | join kind=inner requests on operation_Id | extend exceptionType = tostring(innermostType) | summarize Count=count() by exceptionType, url. Option A is correct because 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'])

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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 17

You are using Application Insights to monitor a web app. You want to automatically analyze and alert on sudden increases in request failure rates, without manually setting static thresholds. Which Application Insights feature should you use?

Accepted Answer

Smart Detection. Smart Detection in Application Insights automatically analyzes telemetry from your web app to detect anomalies, such as sudden increases in request failure rates, without requiring manual static thresholds. It uses machine learning models to adapt to your app's normal behavior and alert on deviations, making it ideal for dynamic monitoring scenarios.

Answer

Application Insights Profiler

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Live Metrics Stream

Answer

Continuous Export

Question 18

You need to emit a custom metric in Application Insights that tracks the number of page views per browser. You expect high volume (millions of events per day). Which API should you use to ensure efficient pre-aggregation and avoid performance issues?

Accepted Answer

GetMetric. C is correct because the GetMetric API (previously known as Pre-Aggregated Metric API) is designed for high-volume telemetry scenarios. It pre-aggregates metrics on the client side before sending them to Application Insights, significantly reducing network traffic and storage costs while avoiding performance bottlenecks from millions of individual events.

Answer

TrackEvent

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TrackMetric

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TrackDependency

Question 19

You are using Application Insights to monitor an ASP.NET Core web API. Users report that a specific endpoint is slow, but you cannot reproduce the issue in development. You need to identify which line of code is causing the delay in production. Which Application Insights feature should you use?

Accepted Answer

Enable Application Insights Profiler.. Application Insights Profiler is designed specifically to trace code-level performance issues in production without requiring code changes or reproducing the problem. It captures detailed call stacks and timing for each request, allowing you to identify exactly which line of code is causing the delay. This makes it the correct choice for diagnosing a slow endpoint that cannot be reproduced in development.

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Use the Application Insights Map to visualize dependencies.

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Use the Snapshot Debugger to capture debug snapshots on exceptions.

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Create a custom telemetry event in the slow endpoint to log timing data.

Question 20

You have an App Service web app with Application Insights configured. You want to create an alert that fires when the server response time exceeds 2 seconds for a rolling 10-minute window. Which type of alert rule should you create?

Accepted Answer

Metric alert. Metric alerts in Azure Monitor evaluate resource-level performance counters at regular intervals, making them ideal for threshold-based conditions like server response time. Application Insights automatically collects server response time as a pre-aggregated metric, so a metric alert can check whether the average exceeds 2 seconds over a rolling 10-minute window without needing to query raw log data.

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Log alert

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Activity log alert

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Smart detection alert