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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 2: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions |
| Topic 3: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
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NEW QUESTION # 132
A Python API running in ACA must send distributed traces to Azure Monitor.
The API creates spans. However, no traces appear in Azure Monitor.
You need to configure the OpenTelemetry SDK pipeline to export traces to Azure Monitor.
What should you do? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Register a global trace provider # initialize the application TraceProvider. Export traces to Azure Monitor # create/configure AzureMonitorTraceExporter. Connect exporter to provider # add a span processor. Generate spans # use tracer.start_as_current_span(...).
Detailed Explanation: OpenTelemetry tracing is a pipeline: the application obtains a tracer from a configured provider, creates spans, passes ended spans through a span processor, and exports them using the Azure Monitor exporter. Creating spans alone is insufficient; without the provider/processor/exporter chain, no telemetry reaches Azure Monitor. Log sampling and legacy Application Insights TrackEvent calls are unrelated to the required OpenTelemetry trace-export path.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 133
You plan to deploy an Azure Container app.
You need to configure the container app to support session affinity.
Which ingress type and revision mode should you assign to the container app?
Answer: C
Explanation:
HTTP ingress type and single revision mode must be assigned. Session affinity (sticky sessions) requires an HTTP-based proxy layer to track and route client requests to the same replica, which is incompatible with raw TCP ingress. Single revision mode ensures standard traffic flow without multi-revision splitting complexity.
Reference:
https://learn.microsoft.com/en-us/azure/container-apps/ingress-environment-configuration
NEW QUESTION # 134
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: B,D
Explanation:
To build a scalable and reliable asynchronous solution that meets the requirements, you should use an HTTP-triggered function to save images to Azure Blob Storage and enqueue a message, then use a Queue-triggered function to process the image.
1. Decouple via Queue Messaging
HTTP Function: Receives the initial user upload, saves the large image file directly to Azure Blob Storage, and writes a small metadata message (e.g., the blob URI) into an Azure Storage Queue or Azure Service Bus Queue.
Instant Response: The HTTP function immediately returns a 202 Accepted status to the client along with a status-tracking URL. This completely eliminates client timeout issues by shifting heavy work to the background.
2. Leverage Built-In Queue Triggers
Queue-Triggered Function: A second, separate Azure Function is configured to trigger whenever a new message lands in the queue. It downloads the image from Blob Storage and performs the processing.
Automatic Retries: Azure Queue storage and Service Bus triggers feature native retry behavior. If the processing function fails or crashes, the message is automatically returned to the queue to be retried. Persistent failures are automatically moved to a Poison Queue (Dead Letter Queue) after a configured number of attempts (default is 5).
Independent Scaling: Under the Azure Consumption or Premium plan, the Queue-triggered function scales its instances up or down based on the queue length (the backlog of images to process). This scales independently of the HTTP-triggered function, which scales purely based on incoming request volume.
Reference:
https://oneuptime.com/blog/post/2026-02-16-azure-functions-python-http-triggers-blob-storage/view
NEW QUESTION # 135
You are building a semantic search feature for a chatbot. You store document embeddings in Redis.
You review the following Python code that connects to Redis and stores an embedding value:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: Yes; No; Yes.
Detailed Explanation: `HSET` stores the binary embedding as a field in the Redis hash keyed by `doc:1`, so the first statement is true. The code does not create a RediSearch/Redis Query Engine vector index or define a vector field schema; merely storing bytes does not enable similarity search, so the second statement is false.
`EXPIRE doc:1 600` sets a 600-second lifetime, which is ten minutes, making the third statement true.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector search in Azure Managed Redis
NEW QUESTION # 136
Drag and Drop Question
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version 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:
Step 1: Update the image tag in the deployment manifest
This changes the definition file to point to your new version image stored in the Azure Container Registry.
Step 2: Apply the updated manifest
Running kubectl apply -f <manifest.yaml> triggers Kubernetes to natively initiate a zero-downtime rolling update strategy, incrementally replacing old pods with new ones.
Step 3: Verify rollout status
Running kubectl rollout status deployment/<deployment-name> allows you to track and ensure the new version deploys successfully without failure.
Reference:
https://learn.microsoft.com/en-us/azure/aks/tutorial-kubernetes-deploy-application
NEW QUESTION # 137
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