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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
| Topic 2: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
| Topic 3: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
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NEW QUESTION # 96
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Trigger scaling based on queue depth: Azure Service Bus scaler
* Deallocate instances when the message queue is empty: Minimum replicas = 0
* Prevent uncontrolled burst scaling: Maximum replicas = 10
The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.
To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.
Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.
Study Guide references: AKS # KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.
NEW QUESTION # 97
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version by using a Deployment manifest that supports rolling updates without interrupting traffic.
You need to perform a rolling update by using a manifest file.
Which three 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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 98
Drag and Drop Question
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.
Answer:
Explanation:
Explanation:
In the azure-cosmos Python SDK, you do not use an explicit "InitializeDatabaseProxy" method.
Instead, you use the client to get a database reference, and then use that database reference to get a container reference.
Step 1: Initialize CosmosClient
You must first establish the root connection using the client object with your account endpoint and credentials.
Step 2: Specify the database name
From the client, you call client.get_database_client(database_name) to target your specific database resource.
Step 3: Obtain the container reference
From the database proxy object, you call database.get_container_client(container_name) to reference the specific data container.
Step 4: Run the Query
Finally, you execute the SQL operation directly on that container using container.query_items().
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/quickstart-python
NEW QUESTION # 99
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Detailed Explanation: Azure Container Apps uses KEDA-based scale rules for event-driven workloads. A Service Bus scaler can monitor queue depth and create replicas only when messages are pending. To meet the explicit cost requirement, the scale configuration must permit a minimum replica count of zero so all active replicas can terminate when the queue is empty. Increasing the maximum replica count changes peak capacity, and HTTP ingress concurrency is not the correct scaling signal for a background Service Bus consumer.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Set scaling rules in Azure Container Apps
NEW QUESTION # 100
Your organization requires that all prompts and completions sent to Azure OpenAI be retained for zero data logging beyond what's required for abuse monitoring, per contractual requirements.
What should you do?
Answer: C
Explanation:
Customers with qualifying requirements can apply to modify abuse monitoring and human review data logging for an Azure OpenAI resource through Microsoft's formal request process, which is the supported path for controlling this behavior - it isn't a client-side setting.
NEW QUESTION # 101
......
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