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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Implement Azure AI solutions- Implement knowledge mining with Azure AI Search
- Implement computer vision solutions
- Implement natural language processing solutions
- Implement generative AI solutions using Azure OpenAI
Plan and manage Azure AI solutions- Select appropriate Azure AI services
- Plan security and compliance requirements
- Monitor and optimize AI solutions
Implement and monitor AI workloads- Deploy AI models and services
- Monitor performance and troubleshoot issues

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. Drag and Drop Question
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
- Reference the Azure Key Vault without requiring any changes to the
Java code.
- Dynamically add and remove instances of the Azure Functions host
based on the number of incoming application events.
- Ensure that instances are perpetually warm to avoid any cold starts.
- Connect to a VNet.
- Authentication to the Azure Key Vault instance must be removed if the Azure Functions application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
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.


2. 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 plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Embed the API key as a hardcoded environment variable in the Dockerfile.
Does the solution meet the goal?

A) Yes
B) No


3. 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 plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?

A) Yes
B) No


4. 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.


5. You are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods. Which deployment type should you choose?

A) Batch deployment
B) Dedicated managed compute with fixed capacity
C) Standard (pay-as-you-go) deployment
D) Provisioned Throughput Units (PTU)


Solutions:

Question # 1
Answer: Only visible for members
Question # 2
Answer: B
Question # 3
Answer: B
Question # 4
Answer: Only visible for members
Question # 5
Answer: C

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