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The AI-901 Mock Exams not just give you a chance to self-access before you actually sit for the certification exam, but also help you get an idea of the Microsoft exam structure. It is well known that students who do a mock version of an exam benefit from it immensely. Some Microsoft certified experts even say that it can be a more beneficial way to prepare for the Microsoft Azure AI Fundamentals exam than spending the same amount of time studying.
NEW QUESTION # 40
You have a Microsoft Foundry project that contains a vision-enabled GPT model deployment.
You need to develop an application that sends an image and a text question to the model in a single request.
What should you use?
Answer: C
Explanation:
To send an image and a text question to a vision-enabled GPT model deployment within a Microsoft Foundry project in a single request, you should target the Azure OpenAI Responses API via your Foundry Project Endpoint.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/gpt-with-vision
NEW QUESTION # 41
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.
NEW QUESTION # 42
You have a Microsoft Foundry project that contains a vision-enabled chat model deployment.
You are developing a Python application that uses the responses API. The application sends a request that includes a user prompt and a local JPEG image.
You need to include the local image in the request.
Which value should you use for the image input?
Answer: C
Explanation:
The content type value you should use for the image input is input_image, and the payload value must contain a base64-encoded data URI string.
When using the Azure OpenAI Responses API inside a Microsoft Foundry project, multimodal elements are structured uniquely into content blocks. Unlike the standard Chat Completions API (which uses image_url for the content type), the Responses API explicitly requires the following schema for local images:
Required Message Block Structurepythonresponses = client.responses.create( model="your_deployment_name", input=[
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "What is in this image?"
},
{
"type": "input_image", # <--- Use this content type value
"image_url": "data:image/jpeg;base64,/9j/4AAQSkZJRg..." # <--- Base64 string goes here
}
]
}
]
)
Reference:
https://medium.com/@shukclaw/image-text-extraction-with-azure-ai-foundry-7536468c32fd
NEW QUESTION # 43
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 44
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In Microsoft Foundry Agent Service, tool_choice controls whether and how an agent uses tools during a run.
Statement 1: Yes
Setting tool_choice to auto lets the agent/model decide whether to call a tool. This statement is true.
Statement 2: No
Setting tool_choice to none does not mean the model decides whether to call a tool. It means the model will not call a tool. This statement is false.
Statement 3: Yes
Setting tool_choice to required means the agent must call one or more tools during the run. This statement is true.
Microsoft documentation describes the tool_choice values as follows: auto allows the model to decide whether to call tools, none prevents tool calls, and required forces one or more tool calls.
NEW QUESTION # 45
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