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AI-901 Free Demo Questions
A sample from the full question set for this exam.
QUESTION 1 OF 5
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?
Correct Answer: C
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.
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.
QUESTION 2 OF 5
HOTSPOT
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.
HOTSPOT
Select the answer that correctly completes the sentence.
Correct Answer: A
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 OF 5
DRAG DROP
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.
Correct Answer: A
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
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 4 OF 5
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.
Correct Answer: A,B
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.
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 5 OF 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?
Correct Answer: C
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.
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 1 of 5