Microsoft
AI-901
Microsoft Azure AI
Fundamentals
(Updated Version)
Version: Demo
[ Total Questions: 10]
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Certs Exam
Microsoft - AI-901
Category Breakdown
Category
Number of Questions
Implement generative AI apps and agents by using Foundry
5
Implement AI solutions for information extraction by using Foundry
2
Implement AI solutions for text and speech by using Foundry
1
Describe principles of responsible AI
1
Identify AI model components and configurations
1
TOTAL
10
Question #:1 - [Implement generative AI apps and agents by using Foundry]
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry
Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
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.
Answer:
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Explanation
The completed code is:
poller = client.begin_analyze(
analyzer_id="invoice",
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a
poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling
automatically when you call .result() .
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final
analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code
pattern.
Question #:2 - [Implement AI solutions for information extraction by using Foundry]
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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Answer:
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 prebuiltimage.
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 #:3 - [Implement generative AI apps and agents by using Foundry]
You are developing an application that processes voicemail recordings by using Azure Content Understanding
in Foundry Tools.
Which feature does Azure Content Understanding use to convert audio to text?
A.
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A. Voice Live
B. key phrase extraction
C. transcription
D. optical character recognition (OCR)
Answer: C
Explanation
Azure Content Understanding uses transcription to convert audio content, such as voicemail recordings, into
text. Microsoft’s Azure Content Understanding audio documentation states that transcription converts
conversational audio into searchable and analyzable text-based transcripts.
Option A. Voice Live is not the feature used by Content Understanding to convert voicemail recordings into
text.
Option B. key phrase extraction identifies important phrases after text is available; it is not the audio-to-text
conversion step.
Option D. optical character recognition (OCR) is for extracting text from images or documents, not audio.
Therefore, the correct answer is C. transcription .
Question #:4 - [Implement generative AI apps and agents by using Foundry]
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.
Answer: A
Explanation
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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 #:5 - [Implement generative AI apps and agents by using Foundry]
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that sends a message containing text and an image URL. The solution
must ensure the quickest response time.
Which message structure should you include in the request?
A. a user message that includes only a text item and sends an image item in a separate request
B. a system message that includes both a text item and an image item in the content array
C. a system message that includes only a text item and sends an image item in a separate request
D. a user message that includes both a text item and an image item in the content array
Answer: D
Explanation
For a vision-enabled model, Microsoft documentation shows that multimodal prompts can include both text
and image content in the same user message content array. Azure OpenAI vision models accept multimodal
image-and-text input and return natural language responses.
The correct request structure is a user message with a content array containing both items, for example:
{
"role": "user",
"content": [
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{"type": "input_text", "text": "What is in this image?"},
{"type": "input_image", "image_url": image_url}
]
}
This avoids sending separate requests and is therefore the best choice for quickest response time.
A and C are incorrect because they send the text and image separately.
B is incorrect because the image/question should be sent as a user message, while system messages are used
for instructions and behavior.
Question #:6 - [Implement AI solutions for text and speech by using Foundry]
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
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Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports
real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar
streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services.
= No
Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS
implementations for the conversational loop. Microsoft describes live AI voice conversations as combining
speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-tospeech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single
conversational experience. = Yes
This is correct. Microsoft’s guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios
that combine audio with language understanding, reasoning, or generation in a single model call, and Voice
Live supports real-time voice-enabled applications over WebSocket connections.
Question #:7 - [Implement generative AI apps and agents by using Foundry]
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
Answer: C
Explanation
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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.
Question #:8 - [Describe principles of responsible AI]
Select the answer that correctly completes the sentence.
Answer:
Explanation
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The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users
.
Transparency means users should understand when they are interacting with AI, what the system can and
cannot do, and how outputs or predictions are generated at an appropriate level.
The other options map to different responsible AI principles:
protect sensitive user data = Privacy and security
reduce bias in decisions = Fairness
require human oversight = Accountability
Question #:9 - [Identify AI model components and configurations]
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?
A. Evaluator catalog
B. Model leaderboard
C. Compliance
D. Tools
Answer: B
Explanation
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard .
Microsoft documentation states that the model leaderboard helps compare models across quality, safety,
estimated cost, and throughput . It also supports trade-off charts and side-by-side model comparison for
features, performance, and estimated cost.
Why the other options are incorrect:
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A. Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing
LLM costs.
C. Compliance relates to governance and compliance, not model cost comparison.
D. Tools provides Foundry tools, not benchmarked cost comparison across models.
Question #:10 - [Implement AI solutions for information extraction by using Foundry]
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
Answer: B
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 .
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