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| Section | Objectives |
|---|---|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 51
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 # 52
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
* A non-sensitive configuration value named MODEL_VERSION
* A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 53
Hotspot Question
You configure an Event Grid event subscription that routes AI file-upload events to an Azure Function endpoint.
Events must be delivered only in the following conditions:
- The event path starts with /uploads/ai/.
- The payload property data.fileType is "pdf."
You must store undelivered events for later investigation and reprocessing if the subscriber endpoint cannot accept an event after multiple retries.
You need to configure the filtering and reliability settings.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Advanced filter
You should use an advanced filter to check that the path starts with /uploads/ai/ and that the payload property matches pdf.
Box 2: Advanced filter
Payload Property Condition: Use an Advanced Filter. While Subject filters handle prefix/suffix matching and Event Type filters handle the event schema type, Advanced filters are required to inspect deeply nested properties inside the event's data payload.
Box 3: Dead-letter destination
You should configure a Dead-letter destination to store undelivered events for later investigation and reprocessing.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-filter-azure-event-grid-subscription-events-with-advanced-filters/view
https://oneuptime.com/blog/post/2026-02-16-how-to-use-azure-event-grid-with-power-automate-to-react-to-blob-storage-events/view
NEW QUESTION # 54
You are designing Azure Functions for three different backend workloads.
Each workload requires one of the following dispatching models:
- Return an immediate response to a client.
- Process background work from a queue.
- Run code on a fixed schedule.
You need to select the trigger for each requirement.
Which triggers should you select? To answer, move the appropriate triggers to the correct requirements. You may use each trigger 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:
* Return an immediate response to a client: HTTP trigger
* Process background work from a queue: Queue Storage trigger
* Run code on a fixed schedule: Timer trigger
Use an HTTP trigger when a function must be invoked by an HTTP request and return a response directly to the calling client. Microsoft documents HTTP triggers as the standard mechanism for building serverless APIs and handling web requests or webhooks. This makes the HTTP trigger the correct fit for synchronous request- response workloads.
For asynchronous background processing, use an Azure Queue Storage trigger . A queue-triggered function runs when messages are added to an Azure Storage queue. This decouples the producer from the consumer and is appropriate for background processing where work can be buffered and processed independently of the original request. Microsoft also documents target-based scaling for Queue Storage triggers on supported Azure Functions hosting plans.
For scheduled execution, use a Timer trigger . Microsoft states that a timer trigger runs a function according to a configured schedule, typically expressed with an NCRONTAB/CRON expression. This makes it the correct choice for periodic maintenance, scheduled synchronization, reporting, or other fixed-time workloads.
Event Grid and Blob Storage triggers are event-driven mechanisms for event notifications and blob changes respectively, but neither matches these three stated dispatching requirements.
Study Guide references: Azure Functions # HTTP triggers; Queue Storage triggers; Timer triggers; event- driven and scheduled execution.
NEW QUESTION # 55
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
* Store the API key as an App Service application setting configured through the Azure Portal.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 56
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