Microsoft Microsoft AI-200 PDF Microsoft Microsoft AI-200 PDF Questions Available Here at: https://www.certification-exam.com/en/dumps/microsoft-exam/ai-200-dumps/quiz.html Enrolling now you will get access to 224 questions in a unique set of Microsoft AI-200 Topic 1, Proseware Inc. Case Study Background Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers. The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations. The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing. Planned Application Architecture Microservices are containerized by using Docker. Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository. Custom images for containerized microservices are stored in Azure Container Registry (ACR). Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated. Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings. Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates. Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes. Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed). An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval. Monitoring: Telemetry generated by Azure resources is sent to Azure Monitor. A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs. Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK instrumentation. Business Requirements Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be automatically generated. Backend API services must scale automatically during business hours. Cold start delay of backend APIs must be minimized. Secrets must be stored outside of container images. Developers must be able to correlate telemetry across Azure Functions hosts and apps. All tracing must be implemented by using OpenTelemetry SDK instrumentation. Development efforts must be minimized. Technical Requirements Container images must be built automatically and validated before code updates are merged into the main branch. Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services. Dependency of image builds on local developer machines must be eliminated. Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue. Azure Cosmos DB for NoSQL RU consumption must be minimized. Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL. The partner-facing containerized webhook service must run on Azure App Service. Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime. All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity. Azure App Service must supply secrets at runtime without relying on external services. Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments. Known issues RU consumption spikes during vector similarity queries. Question 1 Vou need to deploy Azure function resources and apps by using an automated, version-controlled failed pipeline that supports declarative infrastructure deployment. What should you use? Options: A. Local Git deployment B. GitHub Actions C. Azure Functions Core Tools D. Azure CLI Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Answer: B Explanation: The correct answer is B. GitHub Actions. The question describes a deployment approach that is: - Automated - Version-controlled - Pipeline-based - Supports declarative infrastructure deployment - Used for Azure Function resources and apps GitHub Actions fits all of these requirements because it allows you to: - Define CI/CD workflows in YAML files stored in source control - Automate builds, tests, and deployments - Deploy both application code and infrastructure - Work with declarative infrastructure as code, such as ARM templates, Bicep, Terraform, or other IaC tools - Integrate directly with Azure for deploying Azure Functions Why the other options are not correct: A. Local Git deployment - This is a simpler deployment method where code is pushed from a local Git repository to Azure. - It is not a full automated pipeline solution. - It does not strongly support declarative infrastructure deployment. C. Azure Functions Core Tools - These tools are mainly for local development, debugging, running, and publishing Azure Functions. - They are not a pipeline orchestration service. - They do not provide version-controlled automated infrastructure deployment by themselves. D. Azure CLI - Azure CLI is a command-line tool for managing Azure resources. - It can be used in scripts and automation, but it is not itself a deployment pipeline system. - On its own, it does not provide the pipeline structure, version control integration, and declarative workflow management described in the question. Why GitHub Actions is the best fit: GitHub Actions is designed for continuous integration and continuous delivery. It lets you store deployment definitions in the repository, making them version-controlled and repeatable. It also supports declarative deployment workflows for Azure resources and apps, which matches the scenario exactly. So the answer is B. GitHub Actions. Topic 1, Proseware Inc. Case Study Background Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers. The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations. The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing. Planned Application Architecture Microservices are containerized by using Docker. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository. Custom images for containerized microservices are stored in Azure Container Registry (ACR). Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated. Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings. Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates. Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes. Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed). An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval. Monitoring: Telemetry generated by Azure resources is sent to Azure Monitor. A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs. Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK instrumentation. Business Requirements Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be automatically generated. Backend API services must scale automatically during business hours. Cold start delay of backend APIs must be minimized. Secrets must be stored outside of container images. Developers must be able to correlate telemetry across Azure Functions hosts and apps. All tracing must be implemented by using OpenTelemetry SDK instrumentation. Development efforts must be minimized. Technical Requirements Container images must be built automatically and validated before code updates are merged into the main branch. Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services. Dependency of image builds on local developer machines must be eliminated. Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue. Azure Cosmos DB for NoSQL RU consumption must be minimized. Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL. The partner-facing containerized webhook service must run on Azure App Service. Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime. All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ identity. Azure App Service must supply secrets at runtime without relying on external services. Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments. Known issues RU consumption spikes during vector similarity queries. Question 2 DRAG DROP You need to implement trace correlation according to the business requirements. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select. Options: A. Answer: A Topic 1, Proseware Inc. Case Study Background Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers. The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations. The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing. Planned Application Architecture Microservices are containerized by using Docker. Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository. Custom images for containerized microservices are stored in Azure Container Registry (ACR). Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated. Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings. Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes. Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed). An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval. Monitoring: Telemetry generated by Azure resources is sent to Azure Monitor. A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs. Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK instrumentation. Business Requirements Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be automatically generated. Backend API services must scale automatically during business hours. Cold start delay of backend APIs must be minimized. Secrets must be stored outside of container images. Developers must be able to correlate telemetry across Azure Functions hosts and apps. All tracing must be implemented by using OpenTelemetry SDK instrumentation. Development efforts must be minimized. Technical Requirements Container images must be built automatically and validated before code updates are merged into the main branch. Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services. Dependency of image builds on local developer machines must be eliminated. Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue. Azure Cosmos DB for NoSQL RU consumption must be minimized. Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL. The partner-facing containerized webhook service must run on Azure App Service. Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime. All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity. Azure App Service must supply secrets at runtime without relying on external services. Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments. Known issues RU consumption spikes during vector similarity queries. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Question 3 You need to address the known issue resulting from vector similarity queries. Which two actions should you perform? Each correct answer presents part of the solution. Choose two. NOTE: Each correct selection is worth one point. Options: A. Add a composite index on the vector fields and metadata properties of the container. B. Modify the indexing precision of the vector fields. C. Set the account consistency level to Strong. D. Change the vector index type from flat to quantizedFlat or diskANN. Answer: B, D Explanation: The correct answers are B and D. The issue described is a known problem with vector similarity queries, which usually means the current vector indexing approach is not optimal for performance, scalability, or query behavior. To address this, you need to adjust how vectors are indexed and how the vector index is built. Why B is correct: Modifying the indexing precision of the vector fields can improve how vector data is stored and queried. Precision settings affect the quality and efficiency of similarity search. In environments where vector search has known issues, changing precision can help balance accuracy, storage, and query performance. Why D is correct: Changing the vector index type from flat to quantizedFlat or diskANN is a common fix for vector similarity query limitations. A flat index performs brute-force search, which can become inefficient at scale. QuantizedFlat and diskANN are more advanced vector index types designed for better performance and scalability, making them suitable for resolving known vector query issues. Why the other options are incorrect: A. Add a composite index on the vector fields and metadata properties of the container. Composite indexes are used for traditional query optimization on multiple fields, not for vector similarity search. They do not solve vector indexing issues. C. Set the account consistency level to Strong. Consistency level affects read/write consistency, not vector index behavior. It does not address problems with vector similarity queries. Summary: To resolve the known issue, you should: - B. Modify the indexing precision of the vector fields. - D. Change the vector index type from flat to quantizedFlat or diskANN. Topic 1, Proseware Inc. Case Study Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Background Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers. The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations. The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing. Planned Application Architecture Microservices are containerized by using Docker. Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository. Custom images for containerized microservices are stored in Azure Container Registry (ACR). Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated. Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings. Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates. Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes. Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed). An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval. Monitoring: Telemetry generated by Azure resources is sent to Azure Monitor. A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs. Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK instrumentation. Business Requirements Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be automatically generated. Backend API services must scale automatically during business hours. Cold start delay of backend APIs must be minimized. Secrets must be stored outside of container images. Developers must be able to correlate telemetry across Azure Functions hosts and apps. All tracing must be implemented by using OpenTelemetry SDK instrumentation. Development efforts must be minimized. Technical Requirements Container images must be built automatically and validated before code updates are merged into the main branch. Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services. Dependency of image builds on local developer machines must be eliminated. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue. Azure Cosmos DB for NoSQL RU consumption must be minimized. Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL. The partner-facing containerized webhook service must run on Azure App Service. Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime. All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity. Azure App Service must supply secrets at runtime without relying on external services. Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments. Known issues RU consumption spikes during vector similarity queries. Question 4 You need to configure vector embedding updates according to the business and technical requirements. Which information should you use? To answer, select the appropriate option in the answer area. NOTE: Each correct select is worth one point. Options: A. Answer: A Topic 1, Proseware Inc. Case Study Background Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers. The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations. The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing. Planned Application Architecture Microservices are containerized by using Docker. Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Custom images for containerized microservices are stored in Azure Container Registry (ACR). Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated. Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings. Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates. Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes. Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed). An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval. Monitoring: Telemetry generated by Azure resources is sent to Azure Monitor. A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs. Monitoring of Azure Functions is currently implemented hy using Azure Application Insights SDK instrumentation. Business Requirements Embeddings for new or updated Azure Cosmos DB for NoSQL–hosted documents must be automatically generated. Backend API services must scale automatically during business hours. Cold start delay of backend APIs must be minimized. Secrets must be stored outside of container images. Developers must be able to correlate telemetry across Azure Functions hosts and apps. All tracing must be implemented by using OpenTelemetry SDK instrumentation. Development efforts must be minimized. Technical Requirements Container images must be built automatically and validated before code updates are merged into the main branch. Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services. Dependency of image builds on local developer machines must be eliminated. Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue. Azure Cosmos DB for NoSQL RU consumption must be minimized. Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL. The partner-facing containerized webhook service must run on Azure App Service. Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime. All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity. Azure App Service must supply secrets at runtime without relying on external services. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments. Known issues RU consumption spikes during vector similarity queries. Question 5 You need to configure a connection string for the partner-facing service according to the technical requirements. What should you use? Options: A. Azure Key Vault references in App Service settings B. GitHub secrets C. Azure Container Registry Helm chart package D. Dockerfile ENV instructions Answer: A Explanation: The correct answer is A. Azure Key Vault references in App Service settings. Why this is correct: You need to configure a connection string for a partner-facing service, and the technical requirements typically imply that the value should be stored securely and not hard-coded in the application or deployment artifacts. Azure Key Vault references in App Service settings let you place the connection string in Azure Key Vault and then reference it directly from the App Service configuration. This approach is preferred because: - Secrets are not stored in code or in deployment files. - The App Service can retrieve the secret securely at runtime. - You can rotate the secret in Key Vault without changing application code. - It aligns with standard cloud security practices for managing connection strings and credentials. Why the other options are not correct: B. GitHub secrets - GitHub secrets are useful for CI/CD pipelines, but they are not the best choice for runtime configuration of an Azure App Service connection string. - They are meant for repository or workflow automation, not for direct application secret management in Azure hosting. C. Azure Container Registry Helm chart package - A Helm chart package is for deploying and configuring Kubernetes applications. - It is not a secret management mechanism and does not securely store connection strings by itself. D. Dockerfile ENV instructions Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ - Using ENV instructions in a Dockerfile is insecure for secrets. - Environment values in Dockerfiles are part of the image definition and can be exposed in the image history or runtime configuration. - This is not an appropriate way to manage sensitive connection strings. Summary: If you need to configure a secure connection string for an Azure-hosted service, Azure Key Vault references in App Service settings is the best option because it securely stores and injects the secret at runtime. Question 6 You are developing a Java application to be deployed in Azure. The application stores sensitive data in Azure Cosmos DB. You need to configure Always Encrypted to encrypt the sensitive data inside the application. What should you do first? Options: A. Create a Microsoft Entra ID managed identity and assign the identity to a new Azure Key Vault instance. B. Create a new container to include an encryption policy with the JSON properties to be encrypted. C. Create a customer-managed key (CMK) and store the key in a new Azure Key Vault instance. D. Create a data encryption key (DEK) by using the Azure Cosmos DB SDK and store the key in Azure Cosmos DB. Answer: C Explanation: The correct answer is C. Create a customer-managed key (CMK) and store the key in a new Azure Key Vault instance. Always Encrypted in Azure Cosmos DB is designed to protect sensitive data by encrypting it in the application before the data is sent to Cosmos DB. For this to work, you need a key encryption key that can securely protect the data encryption keys used by the application. In Azure, that key is typically a customer- managed key stored in Azure Key Vault. Why C is correct: - A customer-managed key (CMK) is the first thing you need because it serves as the root key for protecting encryption keys used by the application. - Azure Key Vault is the secure place to store and manage that CMK. - Once the CMK is in place, the application can use it to encrypt and decrypt data encryption keys, which are then used to encrypt the sensitive fields in Cosmos DB. Why the other options are not correct: A. Create a Microsoft Entra ID managed identity and assign the identity to a new Azure Key Vault instance. Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ - A managed identity can be used to authenticate the application to Key Vault. - However, this is not the first step for configuring Always Encrypted. - You still need the CMK in Key Vault before the application can use it for encryption. B. Create a new container to include an encryption policy with the JSON properties to be encrypted. - An encryption policy is part of the setup, but it comes after you have established the key management foundation. - Without the CMK and key vault setup, the application cannot properly encrypt and decrypt the sensitive data. D. Create a data encryption key (DEK) by using the Azure Cosmos DB SDK and store the key in Azure Cosmos DB. - This is not correct because data encryption keys are not stored in Cosmos DB as the primary secure key store. - DEKs are protected by a CMK stored in Key Vault, not simply stored in the database itself. Key concept: - CMK in Azure Key Vault is the master key. - DEKs are used to encrypt the actual sensitive fields. - The application uses the CMK to protect the DEKs, enabling Always Encrypted. So the first step is to create and store a CMK in Azure Key Vault, which corresponds to option C. Question 7 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. Scale automatically under variable load. You need to implement a trigger. Which trigger should you implement? Options: A. HTTP B. Azure Queue storage C. Azure Event Grid D. Service Bus topic Answer: A Explanation: The correct answer is A. HTTP An Azure Function app that exposes a public API should use an HTTP trigger because it is designed to respond directly to incoming web requests. Why HTTP is the right choice: Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ - Validate incoming request data and return results immediately to the caller: - HTTP-triggered functions can receive a request, validate the payload, and return a response in the same call. - This matches the behavior of a public API, where the caller expects an immediate result. - Support Microsoft Entra ID authentication: - HTTP endpoints can be secured with Microsoft Entra ID by using App Service authentication, API Management, or custom authorization logic. - This makes HTTP the natural trigger for authenticated API access. - Scale automatically under variable load: - Azure Functions with an HTTP trigger can scale automatically based on incoming request volume. - This is appropriate for public APIs with unpredictable traffic. Why the other options are not correct: - B. Azure Queue storage: - Queue triggers are asynchronous and message-based. - They are not intended for immediate request/response interactions with a caller. - C. Azure Event Grid: - Event Grid is used for event-driven processing, not direct API responses. - It is not suitable for validating client input and returning an immediate result. - D. Service Bus topic: - Service Bus topics are also message-driven and asynchronous. - They are better for decoupled backend processing than for public API endpoints. Summary: If you need a function to behave like a public web API with immediate responses, authentication support, and automatic scaling, the HTTP trigger is the correct implementation. Question 8 DRAG DROP You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events. You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Options: A. Answer: A Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ Question 9 You deploy a private container image from Azure Container Registry (ACR) to App Service. App Service must authenticate to ACR to pull the image. The solution must NOT store static registry credentials. You need to configure the secure image pull authentication. Which configurations should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Options: A. Answer: A Question 10 You are developing several microservices to run on Azure Container Apps. External HTTP ingress traffic has been enabled for the Microservices. A deployed micro service must be updated to allow uses to test new features. You have the following requirements: * Enable and maintain a single URL for the updated microservice to provide to test users. * Update the microservice that corresponds to the current microservice version. You need to configure Azure Container Apps. Which features should configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Options: A. Answer: A Would you like to see more? Don't miss our Microsoft AI- 200 PDF file at: Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/ https://www.certification-exam.com/en/pdf/microsoft-pdf/ai-200-pdf/ Microsoft Microsoft AI-200 PDF https://www.certification-exam.com/