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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 4: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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NEW QUESTION # 33
You are using Azure Monitor Application Insights to collect dependency data.
You must be able to:
- Correlate failed requests with dependency calls during the last hour.
- Calculate the average dependency duration per operation.
You need to construct the KOL query by using the minimum number of statements.
Which three operators should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D,E
Explanation:
To satisfy both requirements with the minimum number of statements using the Kusto Query Language (KQL), you should use the following KQL operators:
where: Filters data by time range (ago(1h)) and request status (success == false).
join: Correlates the requests and dependencies tables using a unique identifier (usually operation_Id).
summarize: Computes the average (avg()) dependency duration grouped by the specific operation (operation_Name).
Reference:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/application-insights-faq
NEW QUESTION # 34
You deploy a new revision of an app in Azure Container Apps.
You need to gradually shift production traffic to the revision while monitoring performance. In addition, you need to be able to quickly roll back. 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,E
Explanation:
Detailed Explanation: Gradual production rollout requires more than one revision to remain active and a mechanism to divide traffic between them. Multiple revision mode permits concurrent active revisions, and traffic splitting lets a specified percentage of requests flow to the new revision while the remainder stays on the proven revision. This also enables rapid rollback by returning traffic to the prior revision. Increasing replicas or restarting a revision changes capacity or process state, not release traffic distribution.
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 | Azure Container Apps revisions
NEW QUESTION # 35
You must ensure an Azure OpenAI-powered application never exceeds a defined token-per-minute budget across all users to avoid runaway costs.
What should you configure?
Answer: C
Explanation:
Configure deployment-level Tokens-per-Minute (TPM) and Requests-per-Minute (RPM) rate limits for the Azure OpenAI model deployment. Azure OpenAI quota is allocated to individual model deployments in TPM units, and Microsoft documents that the TPM assigned to a deployment directly maps to the rate limit enforced for inference requests against that deployment. An associated RPM limit is also enforced according to the model ' s TPM-to-RPM ratio.
Because all application users invoke the same deployment, this enforcement occurs at the deployment boundary rather than independently for each end user. Once request traffic would exceed the configured capacity, Azure applies throttling rather than allowing unrestricted token consumption. Microsoft Foundry additionally supports explicit token-management controls through AI Gateway, where a TPM limit can be set for a model deployment; requests exceeding the limit receive HTTP 429 Too Many Requests responses.
Content filtering governs safety categories, not consumption. An Azure AI Search vector-index limit controls search storage rather than generated tokens. Temperature changes sampling randomness and has no enforcement role in rate or cost control.
Study Guide references: Azure OpenAI quota management; TPM/RPM rate limits; deployment capacity; throttling; Foundry AI Gateway token management.
NEW QUESTION # 36
You are designing an Azure Function that will process orders from a new Azure Service Bus queue.
You need to prevent the system from processing messages more than once and preserve failed messages for investigation.
Which two actions should you implement? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Enable duplicate detection on the Azure Service Bus queue to suppress duplicate sends. Service Bus tracks application-defined MessageId values during the configured duplicate-detection window. If another message with the same MessageId arrives during that interval, Service Bus accepts the send operation but discards the duplicate message. Microsoft specifically recommends using a deterministic business identifier, such as an order ID, as the basis for MessageId when duplicate suppression is required. Duplicate detection must be enabled when the queue is created and is supported in the Standard and Premium tiers.
For failed or poison messages, implement dead-letter handling . A Service Bus queue has a dead-letter subqueue that retains messages that cannot be successfully processed, including messages that exceed the configured maximum delivery count. Those messages remain available for inspection, troubleshooting, correction, or controlled reprocessing rather than being silently discarded.
Sessions provide ordered/FIFO processing and session affinity; they do not primarily prevent duplicates. An HTTP trigger would bypass the required Service Bus processing architecture.
For strict production-grade exactly-once business effects, the consumer should additionally be idempotent , because a message can still be redelivered after a lock or settlement failure.
Study Guide references: Azure Service Bus # duplicate detection; MessageId; dead-letter queues; poison- message handling; reliable asynchronous processing.
NEW QUESTION # 37
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: C
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 # 38
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