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| Section | Objectives |
|---|---|
| Topic 1: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 2: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Develop containerized solutions on Azure | - Implement containerized applications
|
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NEW QUESTION # 36
Hotspot Question
You are creating an app that uses Event Grid to connect with other services. Your app's event data will be sent to a serverless function that checks compliance. This function is maintained by your company.
You write a new event subscription at the scope of your resource. The event must be invalidated after a specific period of time.
You need to configure Event Grid.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Key authentication
For secure, authenticated delivery to your company's serverless function, configure the Event Grid subscription using Key authentication. Select WebHook as the endpoint type and set the destination to your serverless function's HTTPS URL.
Box 2: ValidationCode handshake
For topic publishing in this scenario, you should configure Event Grid to use the ValidationCode handshake. This mechanism ensures that the company's compliance Azure Function securely verifies ownership of the endpoint by echoing a specific validation code back to Event Grid when the new subscription is registered at the scope of the resource.
Reference:
https://learn.microsoft.com/en-us/azure/event-grid/security-authorization
NEW QUESTION # 37
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Answer:
Explanation:
Explanation:
Verified answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
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 | AKS scaling overview | Kubernetes services in AKS
NEW QUESTION # 38
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.
You are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?
Answer: A
Explanation:
Azure Functions application settings are the correct mechanism for keeping environment-specific configuration outside the application source code. Microsoft documents that application settings, including connection strings and other configuration values, are defined separately for each function app . This allows development, test, staging, and production deployments to use different values without modifying the application code.
At runtime, Function app settings are exposed to the application as environment variables , so code can retrieve the required connection string or configuration value without embedding it in a configuration file committed to source control. This directly satisfies the requirement to prevent secrets from being stored in the Git repository.
For production-grade secret management, Azure Key Vault references are often preferable because they provide centralized secret lifecycle management and can be combined with managed identities. However, the question asks only whether the proposed solution meets the stated goals. Storing connection strings in Azure Function application settings does satisfy both: values can vary by deployed environment and remain outside source-controlled application files.
Study Guide references: Azure Functions configuration; application settings; environment variables; secure application configuration.
NEW QUESTION # 39
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: B
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 # 40
Drag and Drop Question
You are developing a new page for a website that uses Azure Cosmos DB for data storage. The feature uses documents that have the following format:
You must display data for the new page in a specific order. You create the following query for the page:
You need to configure an Azure Cosmos DB policy to support the query.
How should you configure the policy? To answer, drag the appropriate JSON segments to the correct locations. Each JSON segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: compositeIndexes
Azure Cosmos DB requires a defined composite index for any query utilizing an ORDER BY clause with multiple properties.
Box 2: descending
In an ORDER BY clause, properties without an explicit direction default to ascending order. Your query ORDER BY p.name, p.city DESC translates to an ascending sort on /name and a descending sort on /city.
Reference:
https://docs.azure.cn/en-us/cosmos-db/index-policy
NEW QUESTION # 41
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