Microsoft AI-901 시험

Question No : 1

You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?

• A.The model training dataset
• B.The Foundry project display name
• C.The exported playground session history
• D.The model endpoint and authentication credentials

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정답:
Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.

Question No : 2

You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.a JSON document that describes the image content
• B.a UTF-8 encoded text description of the image
• C.a publicly accessible URL of the image
• D.a base64 encoded image data string

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정답:
Explanation:
For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft’s Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data. It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL.

Question No : 3

HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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정답:


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.

Question No : 4

You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users.
What should you configure?

• A.tokens per minute (TPM)
• B.system instructions
• C.temperature
• D.max completion tokens

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정답:
Explanation:
To create an AI agent that follows a specific role and behavior, you configure system instructions. Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior.
Option A. tokens per minute (TPM) controls throughput quota, not behavior.
Option C. temperature controls response randomness/creativity, not the agent’s role.
Option D. max completion tokens controls response length, not the agent’s role or behavioral rules.
Therefore, the correct answer is B. system instructions.

Question No : 5

You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?

• A.Call begin_analyze(), and then call poller.result() to retrieve the results.
• B.Submit the PDF to an analyzer and read the results from the request headers.
• C.Use optical character recognition (OCR) to extract text from the PDF and map the fields in the code.
• D.Call analyze() to return the extracted fields synchronously in the same request.

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정답:
Explanation:
Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft’s Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().
Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:
result = poller.result()
Option B is incorrect because extraction results are not read from request headers.
Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.
Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.

Question No : 6

DRAG DROP
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value 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.

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정답:


Explanation:
input=[
{
"role": "user",
"content": [
{"type": "input_text", "text": "What is in this image? Provide 3 bullet points."},
{"type": "input_image", "image_url": image_url},
],
}
]
print(response.output_text)
For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: "input_text", and image content is sent with type: "input_image". Microsoft’s Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.
The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model’s text answer.

Question No : 7

You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?

• A.a transcription workflow in Azure Speech in Foundry Tools
• B.an optical character recognition (OCR)-only document processing pipeline
• C.an analyzer in Azure Content Understanding in Foundry Tools
• D.an Azure AI Search service

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정답:
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft’s Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft’s quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.
Why the other options are incorrect:
A. transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.
B. OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.
D. Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.
Therefore, the correct answer is
C. an analyzer in Azure Content Understanding in Foundry Tools.

Question No : 8

DRAG DROP
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type 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.

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정답:


Explanation:
Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video, and what to extract, such as text, layout, fields, transcripts, and structured JSON output. Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt-image.
For scanned invoices in PDF format, the correct choice is document analyzer, because invoices are document/form content. Microsoft’s Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.
For voicemail recordings in WAV format, the correct choice is audio analyzer, because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.

Question No : 9

HOTSPOT
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point.

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정답:


Explanation:
The correct Python method is:
recognizer.recognize_once()
Completed code:
import azure.cognitiveservices.speech as speechsdk
...
speech_config = speechsdk.SpeechConfig(subscription=key, region=region)
recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config)
recognizer.recognize_once()
Microsoft’s Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.
Why the other options are incorrect:
recognizer.speak_text_async("Ready") is incorrect because speaking text is text-to-speech, not speech-to-text recognition.
recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.
recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to-text transcription of spoken commands.
Therefore, the correct answer is:
recognizer.recognize_once()

Question No : 10

You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.
What should you provision?

• A.an Azure OpenAI resource
• B.a Microsoft Foundry resource
• C.A Microsoft Foundry project
• D.an Azure AI Search service

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정답:
Explanation:
To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.
The invoice scenario is also directly aligned with Content Understanding’s intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.
Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.

Question No : 11

You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?

• A.document analyzer
• B.video analyzer
• C.audio analyzer
• D.image analyzer

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정답:
Explanation:
Voicemail recordings are audio content. Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video, and what elements to extract, including transcripts and structured fields.
Microsoft’s custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.
Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer.

Question No : 12

What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.defines constraints on the model's responses
• B.defines the agent's role and behavior
• C.defines the Azure region where inference occurs
• D.selects which model to use
• E.defines the tokens per minute (TPM) allocation for the model

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정답:
Explanation:
Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.
Option A is correct because instructions can define constraints the model must follow.
Option B is correct because instructions can define the agent’s role and behavior.
Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.

Question No : 13

DRAG DROP
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

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정답:


Explanation:
client = OpenAI(
api_key="...",
base_url="https://resource1.openai.azure.com/openai/v1/",
)
response = client.responses.create(
model="my-mini-gpt",
...
)
For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:
https://<resource-name>.openai.azure.com/openai/v1/
In the question, the Azure OpenAI resource is named Resource1, so the first blank must be resource1. Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.azure.com/openai/v1/ path.
For the model parameter, Azure OpenAI requires the deployment name, not the underlying model name. Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.
The deployed model is gpt-4.1-mini, but the deployment name is my-mini-gpt. Therefore, the second blank must be:
model="my-mini-gpt"
So the correct selections are:
base_url blank = resource1
model blank = my-mini-gpt

Question No : 14

HOTSPOT
Select the answer that correctly completes the sentence.

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정답:


Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous. This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern ― you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
Synchronous is incorrect ― the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
Returned only as unstructured plain text is incorrect ― Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
Limited to OCR-only processing is incorrect ― Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.

Question No : 15

You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?

• A.auto
• B.none
• C.required

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정답:
Explanation:
Microsoft’s Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:
auto means the model decides whether to call tools.
required means the model must call one or more tools.
none means the model does not call tools.
Therefore:
A. auto = Incorrect, because the model may or may not call the Azure function.
B. none = Incorrect, because this prevents tool/function calls.
C. required = Correct, because it forces the agent to call a tool.
The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call