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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 56
You are designing an Azure Function app that processes large image uploads submitted by users through an HTTP endpoint.
The solution must:
* Prevent client timeouts by decoupling image processing from the initial upload request.
* Support automatic retry behavior for failed processing attempts.
* Scale the background processing independently of the rate of incoming HTTP uploads.
You need to design a scalable and reliable asynchronous processing solution.
Which two actions should you implement? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
The correct architecture uses an Azure Storage queue as an asynchronous buffer between the HTTP ingestion path and the computationally expensive image-processing workload. The HTTP-triggered function should accept the upload, enqueue a work message, and return promptly rather than waiting for image processing to finish. Microsoft specifically recommends asynchronous patterns for long-running HTTP operations because an HTTP-triggered Azure Function can encounter the Azure Load Balancer response timeout even though the underlying function continues executing.
A queue-triggered function then performs image processing independently. Azure Functions applies target- based scaling to Azure Queue Storage triggers based on queued workload, allowing worker capacity to expand as the backlog increases rather than being directly tied to HTTP arrival processing.
For reliability, Azure Queue-triggered Functions provide retry behavior. The visibilityTimeout setting controls the delay before a failed message becomes available for another attempt, while maxDequeueCount controls how many processing attempts occur before the message is transferred to the < queue-name > - poison queue. The default maximum is five attempts.
C defeats the asynchronous requirement. D can be useful for tracking upload state, but it does not itself provide workload decoupling, retry handling, or queue-driven scaling.
Study Guide references: Azure Functions # Queue Storage triggers and bindings; event-driven scaling; retry
/poison queue behavior; asynchronous HTTP processing.
NEW QUESTION # 57
Hotspot Question
A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application Insights.
Which operators should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: where
Filter telemetry rows
The where operator filters a table to rows that satisfy a specific predicate. Both box 1 and box 2 require filtering telemetry data to the last hour (timestamp > ago(1h)).
Box 2: where
In the Kusto Query Language (KQL), the where operator filters a table to rows that satisfy a specific predicate or condition. Inside the inner subquery, you need to filter the dependencies telemetry table to only include records from the last hour (timestamp > ago(1)) Box 3: summarize Aggregate calculated data The summarize operator produces a table that aggregates the content of the input table. It is used here with the avg() aggregation function to calculate the average duration grouped by the operation_Name.
Reference:
https://tryhackme.com/room/kqlkustobasicqueries
NEW QUESTION # 58
You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?
Answer: B
Explanation:
Configure a fixed expiration timeout on each Redis key when the summary is created , such as a five- minute TTL. Azure Managed Redis automatically removes a key when its configured timeout expires.
Critically, ordinary read operations do not extend that expiration period, so a summary created at time T will expire approximately five minutes later regardless of how frequently clients retrieve it.
This is the required absolute-expiration behavior. In Redis, applications can establish expiration when writing the value or by using expiration commands after creation. The important design property is that the expiration deadline is not refreshed whenever the key is read.
An allkeys-lru eviction policy addresses memory pressure , not deterministic five-minute expiration. It may evict less recently used keys when memory is constrained, but it does not guarantee removal exactly five minutes after creation. The PERSIST command is directly incorrect because it removes an existing timeout , potentially causing the summary to remain indefinitely. Sliding expiration is also unsuitable because it renews or resets the expiration window based on activity, allowing frequently accessed summaries to remain longer than five minutes.
Therefore, a per-key absolute five-minute expiration is the correct configuration.
Study Guide references: Azure Managed Redis # key expiration; TTL behavior; cache expiration versus eviction; fixed versus sliding expiration.
NEW QUESTION # 59
You are designing a messaging solution by using Service Bus for AI document processing.
You need to ensure that a published message is delivered to multiple independent consumers.
Each consumer must receive their own copy of the message.
Which two Service Bus entities should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Topics and Subscriptions are the two Azure Service Bus entities that fit this scenario.
Topics: The publisher sends the document processing message to a single topic, which acts as the central distribution hub.
Subscriptions: Each independent consumer creates its own individual subscription under that topic. When a message arrives, a copy is forwarded to every independent subscription so each consumer can process their own copy safely and separately.
Reference:
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-queues-topics-subscriptions
NEW QUESTION # 60
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 61
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