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Free AZ-204 Exam Braindumps (page: 18)

Page 18 of 79
PDF with 400 Questions

You are developing an Azure-based web application. The application goes offline periodically to perform offline data processing. While the application is offline, numerous Azure Monitor alerts fire which result in the on-call developer being paged.

The application must always log when the application is offline for any reason.

You need to ensure that the on-call developer is not paged during offline processing.

What should you do?

  1. Add Azure Monitor alert processing rules to suppress notifications.
  2. Disable Azure Monitor Service Health Alerts during offline processing.
  3. Create an Azure Monitor Metric Alert.
  4. Build an Azure Monitor action group that suppresses the alerts.

Answer(s): D

Explanation:

You can use alert processing rules to add action groups or remove (suppress) action groups from your fired alerts.


Reference:

https://docs.microsoft.com/en-us/azure/azure-monitor/alerts/alerts-action-rules



You are developing an online game that includes a feature that allows players to interact with other players on the same team within a certain distance. The calculation to determine the players in range occurs when players move and are cached in an Azure Cache for Redis instance.

The system should prioritize players based on how recently they have moved and should not prioritize players who have logged out of the game.

You need to select an eviction policy.

Which eviction policy should you use?

  1. allkeys-Iru
  2. volatile-Iru
  3. allkeys-lfu
  4. volatile-ttl

Answer(s): A

Explanation:

allkeys-lru: Keeps most recently used keys; removes least recently used (LRU) keys

In general, as a rule of thumb:
Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure.

Incorrect:
volatile-lru: Removes least recently used keys with the expire field set to true.
volatile-lfu: Removes least frequently used keys with the expire field set to true.
volatile-ttl: Removes keys with expire field set to true and the shortest remaining time-to-live (TTL) value.
Use the volatile-ttl if you want to be able to provide hints to Redis about what are good candidate for expiration by using different TTL values when you create your cache objects.


Reference:

https://redis.io/docs/manual/eviction/



You develop an Azure App Service web app and deploy to a production environment. You enable Application Insights for the web app.

The web app is throwing multiple exceptions in the environment.

You need to examine the state of the source code and variables when the exceptions are thrown.

Which Application Insights feature should you configure?

  1. Smart detection
  2. Profiler
  3. Snapshot Debugger
  4. Standard test

Answer(s): C

Explanation:

Exceptions in web applications can be reported with Application Insights. You can correlate failed requests with exceptions and other events on both the client and server so that you can quickly diagnose the causes.

When an exception occurs, you can automatically collect a debug snapshot from your live web application. The debug snapshot shows the state of source code and variables at the moment the exception was thrown. The Snapshot Debugger in Azure Application Insights:

Monitors system-generated logs from your web app.
Collects snapshots on your top-throwing exceptions.
Provides information you need to diagnose issues in production.


Reference:

https://learn.microsoft.com/en-us/azure/azure-monitor/snapshot-debugger/snapshot-debugger



DRAG DROP (Drag & Drop is not supported)
You develop and deploy a Java application to Azure. The application has been instrumented by using the Application Insights SDK.

The telemetry data must be enriched and processed before it is sent to the Application Insights service.

You need to modify the telemetry data.

Which Application Insights SDK features should you use? To answer, drag the appropriate features to the correct requirements. Each feature may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Sampling
Sampling reduces the volume of telemetry without affecting your statistics. It keeps together related data points so that you can navigate between them when you diagnose a problem. In the portal, the total counts are multiplied to compensate for the sampling.

Box 2: Telemetry initializer
Telemetry initializers add or modify properties to any telemetry sent from your app, which includes telemetry from the standard modules. For example, you could add calculated values or version numbers by which to filter the data in the portal.

Box 3: Telemetry processor
Filtering with telemetry processors lets you filter out telemetry in the SDK before it's sent to the server. For example, you could reduce the volume of telemetry by excluding requests from robots. Filtering is a more basic approach to reducing traffic than sampling. It allows you more control over what's transmitted, but it affects your statistics. For example, you might filter out all successful requests.

Incorrect:
* Telemetry channels are responsible for buffering telemetry items and sending them to the Application Insights service, where they're stored for querying and analysis.


Reference:

https://learn.microsoft.com/en-us/azure/azure-monitor/app/api-filtering-sampling



Page 18 of 79



Post your Comments and Discuss Microsoft AZ-204 exam with other Community members:

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