The field of Microsoft is growing rapidly and you need the Microsoft AI-200 certification to advance your career in it. But clearing the Developing AI Cloud Solutions on Azure (AI-200) test is not an easy task. Applicants often don't have enough time to study for the AI-200 Exam. They are in desperate need of real Developing AI Cloud Solutions on Azure (AI-200) exam questions which can help them prepare for the Developing AI Cloud Solutions on Azure (AI-200) test successfully in a short time.
| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
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NEW QUESTION # 80
An HTTP-triggered Azure Function must read a JSON configuration file stored in Azure Blob Storage.
You need to retrieve the configuration file contents.
What should you use?
Answer: D
Explanation:
To read a configuration file stored in Azure Blob Storage from an HTTP-triggered function, you must use an Azure Blob storage input binding.
Input bindings allow you to connect external data to your function natively without writing code to explicitly connect, authenticate, and download data using SDK clients.
Strategic Overview
The Trigger: You will use an HTTP Trigger to execute the function via a web request.
The Input: You will use a Blob Input Binding pointing to the specific JSON path in your storage account.
The Result: The Azure Functions runtime automatically retrieves the file contents and provides them directly to your function code as a string, byte array, or parsed object parameter.
Reference:
https://learn.microsoft.com/en-us/azure/azure-functions/functions-triggers-bindings
NEW QUESTION # 81
You are designing a solution that will use two Azure Functions apps: App1 and App2. App1 is Windows based and will be deployed as code. App2 is Linux based and will be deployed as a container image.
Estimates show that the duration of the request processing for both apps will range from 1 to 10 minutes.
You plan to implement App1 and App2 by using the hosting plan to satisfy the following requirements:
* Request processing can complete within the estimated time range.
* The autoscaling behavior is event driven.
* The upper scaling limit is maximized.
You need to create the hosting plan for the implementation.
Which hosting plan should you create? To answer, move the appropriate hosting plans to the correct apps.
You may use each hosting plan once, more than once, or not at all. You may..
Answer:
Explanation:
Explanation:
pp1: Consumption. App2: Premium.
Detailed Explanation: For App1, the Windows code-based Consumption plan satisfies event-driven autoscaling, allows execution up to the stated ten-minute range, and has the highest scaling ceiling among the listed event-driven choices for this workload. App2 is deployed as a Linux container image; among Premium, Dedicated, and Consumption, Premium is the event-driven plan that supports Linux containerized Functions and permits long-running execution. Dedicated hosting is not the requested event-driven autoscale model. The original Premium/Premium answer therefore did not maximize App1's upper scaling limit.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions scale and hosting
NEW QUESTION # 82
You plan to create a Docker image that runs an ASP.NET Core application named ContosoApp. You have a setup script named setupScript.psl and a series of application files including ContoscApp.dll.
You need to create a Dockerfile document that meets the following requirements:
* Call SQiupScript.psI when the container is built.
* Run CantosoApp.dill when the container starts.
The Dockerfile document must be created in the same folder where ContosoApp.dll and setupScnpt.ps! are stored.
Answer:
Explanation:
Explanation:
NEW QUESTION # 83
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 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: Use App Configuration with Key Vault references to store environment-specific settings and secrets, accessed from the function app by using a managed identity.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Use App Configuration with Key Vault references to store environment-specific settings and secrets, accessed from the function app by using a managed identity.
This is an industry-standard best practice architectural pattern.
Using Azure App Configuration combined with Azure Key Vault references completely satisfies your compliance requirements. It centralizes feature flags and non-sensitive configurations, keeps sensitive data safely out of source control, handles multi-environment deployments cleanly, and eliminates credentials via a passwordless Managed Identity.
Incorrect:
* Store connection strings in the Function app application settings configured in the Azure Portal.
* Store production secrets in environment variables set by the Dockerfile.
Reference:
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
NEW QUESTION # 84
Hotspot Question
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Vector
The correct field type for storing and querying embeddings in Redis is VECTOR.
Box 2: HNSW index
To support low-latency, large-scale approximate nearest neighbor (ANN) vector search in Redis, you should use the HNSW (Hierarchical Navigable Small World) index type with Float32 data type.
Reference:
https://redis.io/docs/latest/develop/ai/search-and-query/vectors/
NEW QUESTION # 85
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