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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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109. Frage
You need to configure image build automation based on the technical requirements.
Which settings should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
* Trigger for container images build: Base image update
* Implementation of image builds: ACR Task
The correct trigger is Base image update because Proseware requires every custom container image to be rebuilt automatically whenever its Docker Hub base image changes. Azure Container Registry Tasks can detect base-image dependencies from the Dockerfile FROM instruction and automatically trigger a rebuild when that base image is updated. Microsoft confirms that ACR Tasks can track base images in public repositories such as Docker Hub and Microsoft Container Registry, as well as images stored in Azure Container Registry.
The build implementation must be an ACR Task . This directly satisfies the technical requirement that image- build automation run inside Azure Container Registry , eliminating dependencies on local developer machines or separate external build services. ACR Tasks provides cloud-based container building and can automate builds based on source-code commits, base-image updates, or scheduled triggers.
A GitHub workflow could orchestrate CI/CD, but using it as the actual image-building implementation would conflict with the explicit requirement that builds execute inside ACR. Docker Compose defines multi- container applications; it is not the native ACR build automation mechanism. A Commit trigger addresses source changes rather than the stated base-image update requirement, while Scheduled builds would introduce unnecessary periodic execution.
Study Guide references: Azure Container Registry Tasks; base-image dependency tracking; automated rebuilds; Docker Hub base-image triggers; cloud-native container builds.
110. Frage
You are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods.
Which deployment type should you choose?
Antwort: B
Begründung:
Choose a Standard pay-as-you-go deployment . Microsoft Foundry documentation identifies Standard deployments as appropriate for low-to-medium volume workloads with high burstiness . Standard uses pay-per-token billing , so charges are based on actual inference consumption rather than continuously reserved model capacity. This makes it well suited to workloads whose traffic varies significantly and may have long idle periods.
By contrast, Provisioned Throughput Units (PTUs) reserve model-processing capacity and are designed for sustained, predictable high-throughput workloads where consistent latency is important. Microsoft states that PTUs are billed based on deployed capacity even when that capacity is not actively serving requests.
Consequently, PTU deployments can be economically inefficient when the endpoint is frequently idle or traffic is highly variable.
A Batch deployment is optimized for asynchronous, non-latency-sensitive processing and is not appropriate for an interactive endpoint receiving unpredictable live traffic. Dedicated managed compute with fixed capacity introduces continuously allocated infrastructure and therefore does not align with the requirement to minimize idle-period cost.
Microsoft also recommends Standard or Global Standard for variable, bursty traffic , while provisioned types are recommended for consistent high-volume workloads.
Study Guide references: Microsoft Foundry Models # Standard deployments; pay-per-token billing; bursty workloads; provisioned throughput; batch processing.
111. Frage
You have container source code stored in a Git repository.
The container registry must automatically build and store a new container image whenever a developer commits code to the Git repository.
You must minimize the use of an external build infrastructure.
You need to configure Azure Container Registry (ACR) to manage the build process natively and automatically.
Which two ACR components should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Antwort: B,C
Begründung:
Azure Container Registry Tasks provides Azure-hosted container build capabilities, eliminating the need to maintain external Docker build servers or dedicated CI build infrastructure. A Quick task uses commands such as az acr build to send the build context to ACR-managed compute, where Azure builds the container image and, by default, pushes the successfully built image directly into the registry. Microsoft describes quick tasks as cloud-based, on-demand build-and-push operations that do not require a local Docker Engine.
For automatic execution after source-code changes, use a Source-triggered task . ACR Tasks can associate a task with a GitHub or Azure Repos repository and automatically execute the image build whenever code is committed to the configured branch. Microsoft documents that when a commit occurs, the source-control trigger causes ACR Tasks to build and push the resulting image.
A Webhook is not the required standalone ACR build component here. ACR Tasks creates and manages the necessary source-repository webhook as part of configuring the source trigger. An Artifact Cache rule caches upstream container artifacts and does not build application images from Git commits.
Study Guide references: Azure Container Registry Tasks # Quick tasks; Automatically triggered tasks; Source-code commit triggers; cloud-based image builds.
112. Frage
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?
Antwort: D
Begründung:
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.
113. Frage
You are developing a Python application that reads data from Azure Cosmos DB for NoSQL by using the azure-cosmos SDK.
You need to connect to an existing account and run a SQL query against an existing container.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Antwort:
Begründung:
Explanation:
* Initialize CosmosClient.
* Specify the database name.
* Obtain the container reference.
* Run the query.
The Python Azure Cosmos DB SDK follows the resource hierarchy Account # Database # Container # Items . The first step is to initialize a reusable CosmosClient with the Azure Cosmos DB account endpoint and an appropriate credential. Microsoft identifies CosmosClient as the primary client for interacting with databases in an account.
Next, specify the existing database by calling client.get_database_client(database_name). This returns a DatabaseProxy representing that database. Importantly, DatabaseProxy should not be instantiated directly ; Microsoft explicitly states that it should be obtained through CosmosClient.get_database_client().
From the database reference, obtain the target container by using database.get_container_client (container_name). Microsoft documents this as the standard way to retrieve an existing container reference.
Finally, execute the SQL query through container.query_items() , which accepts the Cosmos DB for NoSQL query and optional parameters and returns an iterable result set.
Therefore, Initialize DatabaseProxy is the distractor: the SDK creates the DatabaseProxy through get_database_client; application code should not construct it directly.
Study Guide references: Azure Cosmos DB for NoSQL # Python SDK; CosmosClient; database and container clients; ContainerProxy.query_items().
114. Frage
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