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| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
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問題 #119
An AI application uses a database. The database credential rotates every 30 days.
The application currently requires a manual update each time the credential rotates.
You need to ensure that the application always uses the latest secret version without manual updates.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
答案：A,B
解題說明：
Configure a Key Vault rotation policy to automate periodic credential generation and updates.
Retrieve secrets without a version identifier (versionless reference) so the app automatically resolves the newest active version.
Incorrect:
[Not B]
To stop manual updates, you should not retrieve secrets by specifying a version identifier, because locking in a version stops the application from seeing newer updates.
Reference:
https://learn.microsoft.com/en-us/azure/key-vault/secrets/secure-secrets
問題 #120
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.
答案：
解題說明：
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
問題 #121
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.
答案：B,D
解題說明：
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
問題 #122
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?
答案：C
解題說明：
The applicable Azure OpenAI control is Modified Abuse Monitoring . Microsoft documents that customers processing highly sensitive or confidential information can apply to modify the normal abuse-monitoring process when they cannot permit Microsoft to store or conduct human review of prompts and completions.
Approval is subject to Microsoft ' s Limited Access eligibility requirements and must be requested through the designated Microsoft process.
For approved configurations, the abuse-monitoring treatment of prompts and completions is modified so that normal human review does not occur. Microsoft guidance on zero-data-retention scenarios specifically directs eligible customers to request Modified Abuse Monitoring rather than attempting to achieve the requirement by disabling unrelated telemetry services.
Disabling Azure Monitor does not change Azure OpenAI ' s service-side abuse-monitoring policy; it only affects customer-configured operational telemetry. Changing the model version likewise has no direct effect on retention requirements. Storing application data in Cosmos DB controls the application ' s own persistence layer and does not govern Azure OpenAI service processing.
Therefore, among the available choices, A is the required action. More precisely, the relevant request is Modified Abuse Monitoring ; modified content filtering/Guardrails is a separate control unless the organization also requires changes to safety filtering.
Study Guide references: Azure OpenAI # Data privacy and security; Abuse monitoring; Modified Abuse Monitoring; Limited Access requirements.
問題 #123
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.
答案：
解題說明：
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
問題 #124
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