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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Topic 2: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
| Topic 3: Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
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NEW QUESTION # 111
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic refresh.
The solution must support dynamic configuration refresh while ensuring that secrets are NOT stored in App Configuration.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D,E
Explanation:
[A]
Setting up a cache expiration or polling interval enables the application to detect configuration changes periodically without restarting, fulfilling the requirement for dynamic refresh.
[B]
Using Azure Managed Identities eliminates hardcoded credentials. It allows your application to securely authenticate to both Azure App Configuration and Azure Key Vault.
[C]
Moving API keys to Azure Key Vault adheres to security best practices. Azure App Configuration can reference these secrets securely using Key Vault references, ensuring secrets are never stored as plaintext in the configuration store.
Reference:
https://smartbridge.com/azure-ai-foundry-enterprise-guide-2/
NEW QUESTION # 112
You are designing an Azure Function app that exposes a public API.
The solution must:
- Validate incoming request data and return results immediately to the
caller.
- Support Microsoft Entra ID authentication.
- Guarantee idempotent processing when the same request is retried.
- Scale automatically under variable load.
- Avoid duplicate processing.
You need to implement a trigger.
Which trigger should you implement?
Answer: D
Explanation:
An HTTP Trigger combined with Azure Durable Functions (Asynchronous Http Start pattern) and a backing database is the most appropriate architecture to fulfill all your requirements.
Immediate validation: HTTP triggers allow synchronous request-response communication, enabling you to validate payload data and return a 200 OK or 400 Bad Request to the caller instantly.
Entra ID authentication: Azure Functions native App Service Authentication (Easy Auth) integrates directly with HTTP endpoints to enforce Microsoft Entra ID token validation before your code even runs.
Automatic scaling: HTTP-triggered functions scale dynamically out-of-the-box using the Azure Functions Consumption or Premium hosting plans based on incoming web traffic.
Reference:
https://github.com/microsoft/woodgrove-auth-api/blob/main/README.md
NEW QUESTION # 113
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: A,D
Explanation:
The correct architecture uses an Azure Storage queue as an asynchronous buffer between the HTTP ingestion path and the computationally expensive image-processing workload. The HTTP-triggered function should accept the upload, enqueue a work message, and return promptly rather than waiting for image processing to finish. Microsoft specifically recommends asynchronous patterns for long-running HTTP operations because an HTTP-triggered Azure Function can encounter the Azure Load Balancer response timeout even though the underlying function continues executing.
A queue-triggered function then performs image processing independently. Azure Functions applies target- based scaling to Azure Queue Storage triggers based on queued workload, allowing worker capacity to expand as the backlog increases rather than being directly tied to HTTP arrival processing.
For reliability, Azure Queue-triggered Functions provide retry behavior. The visibilityTimeout setting controls the delay before a failed message becomes available for another attempt, while maxDequeueCount controls how many processing attempts occur before the message is transferred to the < queue-name > - poison queue. The default maximum is five attempts.
C defeats the asynchronous requirement. D can be useful for tracking upload state, but it does not itself provide workload decoupling, retry handling, or queue-driven scaling.
Study Guide references: Azure Functions # Queue Storage triggers and bindings; event-driven scaling; retry
/poison queue behavior; asynchronous HTTP processing.
NEW QUESTION # 114
You optimize an AI inference API that uses Redis caching.
You must reduce the risk of serving outdated data while minimizing cache management overhead.
You need to implement the caching strategy that satisfies the requirements.
What should you do?
Answer: B
Explanation:
Trigger invalidation when source data changes is the best action.
Data Freshness: This is a proactive cache-aside or write-through approach. By explicitly deleting or updating the specific Redis key when the underlying database or model data changes, you completely eliminate the risk of serving stale data.
Low Overhead: It keeps cache management overhead minimal because invalidation logic is execution-dependent (event-driven). Instead of constantly updating expirations or relying on heavy computing logic, you only trigger an action when a write/change event occurs.
Incorrect:
[not B]
Set eviction policy to allkeys-lru:
This policy is a memory management feature, not a data-freshness strategy. It only deletes the Least Recently Used (LRU) keys when your Redis instance runs out of RAM. If memory usage is stable, outdated data will sit in the cache forever, posing a major risk of serving stale inferences.
[not D]
Reset expiration on each read (Sliding TTL):
This actively increases the risk of serving outdated data. If a specific AI response is highly popular and read continuously, its expiration window will extend indefinitely. The data remains stuck in the cache even if the source data undergoes critical updates.
Reference:
https://oneuptime.com/blog/post/2026-01-30-llm-caching-strategies/view
NEW QUESTION # 115
Drag and Drop Question
You plan to create a Docker image that runs an ASP.NET Core application named ContosoApp.
You have a setup script named setupScript.ps1 and a series of application files including ContosoApp.dll.
You need to create a Dockerfile document that meets the following requirements:
- Call setupScript.ps1 when the container is built.
- Run ContosoApp.dll when the container starts.
The Dockerfile document must be created in the same folder where ContosoApp.dll and setupScript.ps1 are stored.
Which five commands should you use to develop the solution? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: FROM microsoft/aspnetcore:latest
Sets the base image for the container.
Base First: You must always declare the base image (FROM) before running any other commands.
Step 2: WORKDIR /apps/ContosoApp
Creates and sets the working directory inside the container.
Directory and Copy: You need to set the working directory (WORKDIR) before copying files (COPY) so Docker knows exactly where to put them.
Step 3: COPY ./ .
Copies the application files and script from your local folder into the working directory.
Step 4: RUN powershell ./setupScript.ps1
Executes the script during the image build process.
Build vs. Run: The RUN command happens while building the image, so the script completes its setup before the final image is saved. The CMD instruction does not execute anything during the build; it simply configures the starting point for when the container is actually launched.
Step 5: CMD ["dotnet", "ContosoApp.dll"]
Defines the default command to run your application when the container starts.
Reference:
https://azuretar.com/using-startup-command-to-pass-command-line-arguments-to-azure-app-service-for-linux/
NEW QUESTION # 116
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