AI-102 Plan and manage an Azure AI solution Practice Test 18 — 15 Questions

Question 1

You are an AI engineer at Contoso. Contoso has a Microsoft Copilot for Microsoft 365 deployment. They want to build a custom copilot in Microsoft Copilot Studio that can answer questions about their internal IT support knowledge base stored in a SharePoint Online document library. The knowledge base includes hundreds of PDF and Word documents. Requirements: - The copilot must only answer from the approved knowledge base documents. - Responses must be grounded in the documents and include citations. - The solution must use generative answers with a prebuilt AI model (no custom model training). - Authentication must be via Microsoft Entra ID with single sign-on (SSO). - The copilot should be published to a Microsoft Teams channel. You need to recommend the minimal configuration steps. What should you do?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint document library as a knowledge source. Configure authentication with Microsoft Entra ID. Enable generative answers. Publish to the Teams channel.. The minimal configuration is to create a copilot in Copilot Studio, add the SharePoint document library as a knowledge source, configure authentication with Microsoft Entra ID, enable generative answers, and publish to Teams. This uses built-in capabilities, requires no custom model training, and meets all requirements including grounding with citations and SSO.

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Create a custom Azure AI Language model using the documents. Then build a bot with Azure Bot Service and connect it to Copilot Studio.

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Use Power Virtual Agents to create a bot. Add a custom entity to map document content. Use Power Automate to retrieve documents.

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Use Azure AI Search to index the documents. Create a custom connector in Copilot Studio to query the search index. Configure authentication and publish to Teams.

Question 2

You are planning a solution that uses Azure AI Language to analyze customer feedback from social media posts. The solution must: - Detect sentiment (positive, negative, neutral) for each post. - Extract key phrases. - Support English and Spanish languages. - Run asynchronously for a batch of 10,000 posts. - Use the least expensive option that meets requirements. What should you do?

Accepted Answer

Use the Azure AI Language service with the built-in sentiment analysis and key phrase extraction capabilities. Process posts in batches using the async API.. Azure AI Language's built-in sentiment analysis and key phrase extraction natively support both English and Spanish, and the async batch API is designed for high-volume processing (e.g., 10,000 posts) at a lower cost than per-document calls. This approach meets all requirements without custom models or translation overhead.

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Build a custom text classification model in Azure AI Language to detect sentiment and extract key phrases.

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Use the Azure AI Language service with the single-document API for each post.

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Use Azure AI Translator to translate all posts to English, then use Azure AI Language for analysis.

Question 3

You are implementing a chatbot using Microsoft Copilot Studio that helps employees find company policies. The chatbot must: - Use generative answers based on a SharePoint Online site. - Only respond with information from approved policy documents. - Include citations in responses. - Be accessible from Microsoft Teams. - Require no custom code. What should you do?

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint site as a knowledge source. Enable generative answers with citations. Publish to Teams.. Microsoft Copilot Studio natively supports adding a SharePoint Online site as a knowledge source, enabling generative answers that retrieve and cite only approved policy documents. It requires no custom code, automatically includes citations in responses, and can be published directly to Microsoft Teams, fulfilling all stated requirements.

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Use Power Automate to retrieve documents and feed them to Azure OpenAI. Build a custom connector for Teams.

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Build a bot using Azure Bot Service and QnA Maker. Train it with the policy documents. Deploy to Teams.

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Create a custom GPT in Azure OpenAI Studio. Upload the policy documents. Deploy via Azure API Management and expose to Teams.

Question 4

You are planning a new Azure AI solution that will use several Azure AI services, including Azure AI Vision and Azure AI Language. The solution must allow developers to authenticate by using their Microsoft Entra ID credentials without storing service keys in code. You need to configure the Azure AI services resource to support this authentication method. What should you do?

Accepted Answer

Configure the Azure AI services resource to disable local authentication and grant developers the Cognitive Services User role on the resource.. To allow developers to authenticate with Microsoft Entra ID, you must disable key-based authentication on the Azure AI services resource and assign the appropriate role, such as Cognitive Services User, to the developers. This ensures that access is granted through Entra ID tokens rather than shared keys, aligning with security best practices.

Answer

Enable local authentication on the Azure AI services resource and distribute the keys to developers.

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Store the Azure AI services keys in Azure Key Vault and provide developers access to the vault.

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Create a managed identity for the Azure AI services resource and assign it the Cognitive Services User role.

Question 5

You are deploying an Azure AI solution that must process images stored in an Azure Blob Storage account. The solution uses the Computer Vision API and must be able to access the images without exposing storage account keys in code. You need to configure authentication. What should you do?

Accepted Answer

Assign a managed identity to the Azure resource hosting the solution and grant it the Storage Blob Data Reader role on the storage account.. Using a managed identity with the appropriate RBAC role allows the solution to authenticate to Blob Storage without embedding secrets. The Storage Blob Data Reader role grants the necessary read access. This method is secure, requires no credential management, and aligns with Azure best practices for passwordless authentication.

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Store the storage account key in Azure Key Vault and retrieve it at runtime using the application's service principal.

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Use a shared access signature (SAS) token generated with the storage account key and embed it in the application configuration.

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Enable anonymous read access on the blob container and configure the Computer Vision client to use the public URLs of the images.

Question 6

You are deploying an Azure AI solution that uses multiple Azure AI services. You need to monitor the solution and receive alerts when the service quota for transactions per second (TPS) is exceeded. What should you use?

Accepted Answer

Azure Monitor metrics and alerts. Azure Monitor metrics and alerts allow you to track the transaction rate and configure alerts when thresholds are breached. By using metrics such as TotalCalls or Throttling, you can receive notifications when TPS limits are exceeded, enabling timely action.

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Azure Service Health

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Azure Advisor

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Azure Cost Management

Question 7

You are designing an Azure AI solution that uses an Azure OpenAI resource. The solution must allow developers to call the model from a web app without embedding API keys in client-side code. You need to ensure that the web app can authenticate to the Azure OpenAI resource. What should you implement?

Accepted Answer

Enable managed identity on the web app and assign the Cognitive Services OpenAI User role to the identity on the Azure OpenAI resource.. Using a managed identity with the appropriate Azure RBAC role allows the web app to authenticate to Azure OpenAI without any secrets. The Cognitive Services OpenAI User role provides the necessary permissions to invoke the model. This approach is secure, scalable, and aligns with Azure best practices for keyless authentication.

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Configure the web app to use the Azure OpenAI API key and rotate it every 24 hours using Azure Automation.

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Store the Azure OpenAI key in Azure Key Vault and have the web app retrieve it at runtime.

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Generate a shared access signature (SAS) token for the Azure OpenAI resource and pass it in the request header.

Question 8

You are designing an Azure AI solution that uses Azure AI Search. The solution must ensure that only authorized users can query the search index, and that users can only access documents they are permitted to see. You need to implement document-level access control. What should you do?

Accepted Answer

Implement security filters in queries by using the user's identity and a field in the index that maps to allowed groups.. To enforce document-level access control in Azure AI Search, you must implement security filters. This involves adding a field to the index that specifies which users or groups can access each document, and then applying a filter in queries based on the authenticated user's identity. This ensures that users only retrieve documents they are permitted to see.

Answer

Enable Azure Private Link for the search service and restrict access to the corporate network.

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Use customer-managed keys (CMK) to encrypt the index and control access to the keys.

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Use Azure role-based access control (RBAC) to assign the Search Index Data Reader role to users.

Question 9

You are deploying an Azure AI solution that uses multiple Cognitive Services resources. You need to ensure that the solution can be deployed to multiple regions and that each region has its own endpoint and key. What should you use to manage the deployment?

Accepted Answer

Azure Resource Manager (ARM) templates with parameters for region-specific values.. ARM templates are the recommended infrastructure-as-code tool for deploying Azure resources consistently across multiple regions. By using parameters, you can customize region-specific settings such as location, endpoint names, and SKUs. This approach ensures repeatability and simplifies management of multi-region deployments.

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A single Cognitive Services resource with multiple endpoints configured in the application code.

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Azure Policy definitions that enforce the creation of Cognitive Services resources in specific regions.

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Azure CLI scripts that create resources in each region sequentially.

Question 10

You are planning to deploy an Azure AI solution that uses multiple Azure AI services. You need to monitor the solution for performance and operational issues. You want to collect metrics and logs from all the services and analyze them in a central location. What should you use?

Accepted Answer

Azure Monitor. Azure Monitor is the correct choice because it is the comprehensive monitoring solution for collecting and analyzing metrics and logs from Azure resources, including AI services. It enables centralized monitoring, alerting, and diagnostics, which are essential for maintaining the performance of a multi-service AI solution.

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Azure Advisor

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Azure Service Health

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Azure Cost Management

Question 11

You are planning to deploy an Azure AI solution that uses an Azure AI Services multi-service resource. The solution must be deployed across multiple Azure regions to provide high availability. You need to ensure that the solution can fail over automatically if one region becomes unavailable. What should you do?

Accepted Answer

Deploy the Azure AI Services resource in two regions and configure a Traffic Manager profile with priority routing.. Deploying the resource in two regions and using Traffic Manager with priority routing ensures that traffic is directed to the primary region and automatically redirected to the secondary if the primary fails. This provides the required high availability and automatic failover. Other options either replicate data without failover or rely on DNS without health checks.

Answer

Deploy the Azure AI Services resource in one region and configure a custom domain with multiple CNAME records.

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Deploy the Azure AI Services resource in two regions and use Azure Front Door with session affinity enabled.

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Deploy the Azure AI Services resource in one region and enable geo-redundant storage for the resource.

Question 12

You are deploying an Azure AI solution that uses Azure AI Search. The solution must be able to access the search service from an Azure virtual network without traversing the public internet. You need to configure the search service to meet this requirement. What should you do?

Accepted Answer

Create a private endpoint for the search service and configure the virtual network to use it.. To access Azure AI Search privately from a virtual network, you must use a private endpoint, which assigns a private IP address from your subnet to the search service. This ensures all traffic remains within the Azure backbone. Other options either use public internet or do not provide a private IP.

Answer

Configure the search service to use an IP firewall and add the virtual network's public IP address.

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Deploy the search service with a public endpoint and use Azure Front Door to route traffic privately.

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Enable a service endpoint for Azure AI Search on the virtual network subnet.

Question 13

You are planning to deploy an Azure AI solution that uses Azure AI Language to analyze text. The solution must be able to process a high volume of requests and provide a service-level agreement (SLA) for availability. You need to choose the appropriate pricing tier. What should you do?

Accepted Answer

Use the Standard (S) tier for the Azure AI Language resource.. The Standard tier is designed for production workloads, offering higher throughput limits and an SLA. The Free tier is for evaluation only and lacks an SLA. Therefore, the Standard tier is necessary to meet the high-volume and availability requirements.

Answer

Use the Free (F0) tier for each Azure AI Language resource to minimize costs.

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Use the Standard (S) tier only for development and switch to Free (F0) for production to save costs.

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Use multiple Free (F0) resources and load-balance requests across them.

Question 14

You are deploying an Azure AI Language resource that will process customer feedback. The resource must be accessible only from a specific Azure virtual network and must allow access from an on-premises network via a site-to-site VPN. You need to configure network security for the resource. What should you do?

Accepted Answer

Configure a private endpoint for the Azure AI Language resource and disable public access.. A private endpoint is the correct solution because it assigns a private IP address from your virtual network to the Azure AI Language resource, allowing secure access from both the VNet and on-premises via VPN. Disabling public access ensures the resource is not reachable from the internet. This configuration satisfies the network isolation requirements.

Answer

Use Azure Private Link to create a private endpoint for the Azure AI Language resource and enable public access for on-premises clients.

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Enable public access and configure IP firewall rules to allow only the on-premises public IP address.

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Configure a service endpoint for Azure AI Language on the virtual network subnet and enable public access for on-premises clients.

Question 15

You are building an Azure AI solution that uses Azure AI Language to analyze customer feedback stored in an Azure Blob Storage container. The container contains 500,000 small text documents. You need to minimize the total time required to analyze all documents and minimize the number of API calls. What should you do?

Accepted Answer

Use the Analyze Text API asynchronously by submitting a batch job that references the documents in Azure Blob Storage.. For large volumes of documents already stored in Azure Blob Storage, the asynchronous Analyze Text API is the intended mechanism. It accepts a batch job that references the storage container, processes documents in parallel, and returns results for retrieval. This approach reduces the number of API calls to a few (submit and poll) and shortens overall processing time compared with synchronous per-document calls.

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Submit a separate synchronous request for each document by using the Analyze Text API.

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Use Azure AI Document Intelligence to extract text from each document and then call the Azure AI Language API for each extracted document.

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Combine all documents into a single request by using the Analyze Text API with a custom text analytics task.