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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

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

2. You process Azure Service Bus messages that require a dependent external API call.
If the API is temporarily unavailable, you must delay processing of the message without incrementing the delivery count.
You need to find a way to process the message when the API is available while keeping the message accessible.
Which message action should you perform?

A) Dead-letter
B) Defer
C) Abandon
D) Complete

3. You need to translate real-time spoken customer conversations from English to Spanish text during a support call, with minimal latency. Which Azure AI service should you use?

A) Azure AI Translator (text translation REST API)
B) Azure AI Document Intelligence
C) Azure AI Speech (Speech Translation)
D) Azure AI Language (Text Analytics)

4. You need to detect whether an uploaded image contains adult or violent content before allowing it to be posted to a public forum. What should you use?

A) Azure AI Content Safety image moderation
B) Azure AI Face API
C) Azure AI Document Intelligence
D) Azure AI Custom Vision object detection

5. 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 in Azure Key Vault and reference it from an App Service application setting.
Does the solution meet the goal?

A) Yes
B) No

Solutions: