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| Section | Objectives |
|---|---|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Connect to and consume Azure services | - Integrate Azure services
|
>> Reliable AI-200 Test Topics <<
At DumpTorrent, we are proud to offer you actual AI-200 exam questions in our Microsoft AI-200 practice exam material. This actual study material has been checked and approved by leading professionals in the field. A team of over 90,000 experts and professionals have collaborated to design the Developing AI Cloud Solutions on Azure (AI-200) exam material, ensuring that you receive both theoretical knowledge and practical insights to excel in the Developing AI Cloud Solutions on Azure exam.
NEW QUESTION # 91
You are implementing an application by using Azure Event Grid to push near-real-time information to customers.
You have the following requirements:
* You must send events to thousands of customers that include hundreds of various event types.
* The events must be filtered by event type before processing.
* Authentication and authorization must be handled by using Microsoft Entra ID.
* The events must be published to a single endpoint
You need to implement Azure Event Grid
Solution: Publish events to a partner topic. Create an event subscription for each customer.
Does the solution meet the goal?
Answer: A
Explanation:
Detailed Explanation: The proposed partner-topic design does not match the stated need for one publisher endpoint that can fan events into thousands of logically distinct customer topics. Event Grid domains are specifically designed to expose a single publishing endpoint while containing large numbers of topics, with subscriptions and filtering applied at the topic level. Partner topics are intended for partner-published SaaS events and do not substitute for the domain architecture required by this high-scale custom publishing scenario.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Event Grid event domains | Authenticate Event Grid publishing with Microsoft Entra ID
NEW QUESTION # 92
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification.
What should you implement?
Answer: A
Explanation:
A retrieval-augmented generation (RAG) architecture is the correct mechanism when generated answers must be traceable to specific source documents. RAG first retrieves relevant document chunks from a search index or other knowledge source, supplies those chunks to the language model as grounding context, and then generates an answer based on that retrieved information. Microsoft specifically documents that RAG enables responses containing citations back to source content .
For reliable citation generation, each retrieved chunk should carry identifying metadata such as a source identifier, document title, section, page, or URL . Microsoft's Azure RAG guidance recommends labeling retrieved chunks and including source metadata with them, then explicitly instructing the model to cite the corresponding sources in its response. This allows users to verify individual claims against the underlying documents.
Modern Azure AI Search agentic retrieval can also return structured references and grounding metadata , specifically designed for citation linking.
Increasing top_p only alters token sampling. Content filtering addresses safety, not provenance. Using a base model without retrieval provides no authoritative document linkage.
Study Guide references: RAG architecture; grounding data; retrieval metadata; Azure AI Search references; prompt engineering for citations.
NEW QUESTION # 93
Hotspot Question
You are reviewing the message-processing code in a backend worker service.
You review the following Python code that initializes and starts a Service Bus processor.
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:
Box 1: No
No, messages are not removed from the queue as soon as they are received when using PEEK_LOCK mode.
How PEEK_LOCK Works
The Lock: The Service Bus processor locks the message exclusively for your worker for a specific duration.
The Invisible State: Other workers cannot see or process the message while the lock is active.
The Removal: The message is only permanently removed from the queue after it is successfully processed and explicitly completed.
Box 2: Yes
Yes, the message can be retried by another consumer if it fails due to a TransientServiceError.
Explicit abandonment: The code calls receiver.abandon_message(msg) when catching a TransientServiceError.
Lock release: Abandoning a message immediately releases the PEEK_LOCK held by the current worker.
Immediate availability: The message returns to the active queue and becomes instantly visible to other competing consumers.
Box 3: No
No, messages with invalid JSON payloads will not be retried until the maximum delivery count is reached. They will be sent to the dead-letter queue (DLQ) immediately on the very first attempt.
Immediate Dead-Lettering: The code catches the json.JSONDecodeError and explicitly calls receiver.dead_letter_message(msg).
Instant Termination: This API call tells Azure Service Bus to move the message to the DLQ right away, bypassing the normal delivery count logic.
No Re-delivery: Because the message is moved to the DLQ, the broker removes it from the main queue, stopping any further processing attempts.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-servicebus/azure.servicebus.aio.servicebusreceiver
https://learn.microsoft.com/en-us/azure/service-bus-messaging/service-bus-python-how-to-use-queues
NEW QUESTION # 94
An HTTP-triggered Azure Function must read a JSON configuration file stored in Azure Blob Storage.
You need to retrieve the configuration file contents.
What should you use?
Answer: A
Explanation:
To read a configuration file stored in Azure Blob Storage from an HTTP-triggered function, you must use an Azure Blob storage input binding.
Input bindings allow you to connect external data to your function natively without writing code to explicitly connect, authenticate, and download data using SDK clients.
Strategic Overview
The Trigger: You will use an HTTP Trigger to execute the function via a web request.
The Input: You will use a Blob Input Binding pointing to the specific JSON path in your storage account.
The Result: The Azure Functions runtime automatically retrieves the file contents and provides them directly to your function code as a string, byte array, or parsed object parameter.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-triggers-bindings
NEW QUESTION # 95
An Azure Storage Blob upload must trigger backend processing.
The solution must:
- Avoid polling.
- Minimize latency.
- Trigger automatically when the upload occurs.
You need to ensure that processing starts as soon as a file is uploaded.
Which trigger should you implement?
Answer: C
Explanation:
Azure Event Grid is the best trigger type to use because it pushes real-time event notifications with minimal latency and completely avoids polling.
Push-based model: Sends events instantly as blobs are created.
Lowest latency: Delivers notifications in milliseconds.
No polling overhead: Saves resources compared to timer or queue polling.
How It Works
A user uploads a file to Azure Blob Storage.Blob Storage fires an Microsoft.Storage.BlobCreated event.
Event Grid pushes this event directly to your backend endpoint (such as an Azure Function or Webhook).Processing starts right away.
Reference:
https://damle-amit075.medium.com/azure-databricks-auto-loader-plugging-the-gap-for-batch-loads-72585b0f158e
NEW QUESTION # 96
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