Quiz about Certification Exam, Microsoft, AI-901: Microsoft Azure AI Fundamentals (Updated Version)

Question 1

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?

Accepted Answer

required

Answer

auto

Answer

none

Question 2

Select the answer that correctly completes the sentence.

$\"Certification$

Accepted Answer

$\"Certification$

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 3

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.

$\"Certification$

Accepted Answer

client = OpenAI(
api_key=\"...",
base_url="[https://resource1.openai.azure.com/openai/v1/",](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://.openai.azure.com/openai/v1/](https://.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 4

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.

Accepted Answer

defines constraints on the model's responses

Accepted Answer

defines the agent's role and behavior

Answer

defines the Azure region where inference occurs

Answer

selects which model to use

Answer

defines the tokens per minute (TPM) allocation for the model

Question 5

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?

Accepted Answer

audio analyzer

Answer

document analyzer

Answer

video analyzer

Answer

image analyzer

Question 6

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?

Accepted Answer

a Microsoft Foundry resource

Answer

an Azure OpenAI resource

Answer

A Microsoft Foundry project

Answer

an Azure AI Search service

Question 7

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.

$\"Certification$

Accepted Answer

$\"Certification$

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 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.

$\"Certification$

Accepted Answer

$\"Certification$

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 9

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?

Accepted Answer

an analyzer in Azure Content Understanding in Foundry Tools

Answer

a transcription workflow in Azure Speech in Foundry Tools

Answer

an optical character recognition (OCR)-only document processing pipeline

Answer

an Azure AI Search service

Question 10

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.

$\"Certification$

Accepted Answer

$\"Certification$

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.

Quiz AI-901: Microsoft Azure AI Fundamentals (Updated Version)

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