Exam: AI-901

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AI-901 Microsoft Azure AI Fundamentals (Updated Version) – Complete Guide

The AI-901 Microsoft Azure AI Fundamentals is an entry-level certification offered by Microsoft that validates your understanding of Artificial Intelligence (AI) and Azure AI services. This exam is ideal for beginners, students, and professionals who want to explore AI concepts without requiring deep technical experience.

This updated version focuses on real-world AI applications, cloud-based AI solutions, and Microsoft Azure tools like Azure Machine Learning, Cognitive Services, and AI workloads.

What the exam covers

AI-900 focuses on describing, rather than deeply implementing, AI workloads and Azure services. The current skills outline includes:

AI workloads and considerations (15–20%)
Common AI scenarios, types of AI (ML, computer vision, NLP, conversational AI).
Principles of responsible AI, fairness, privacy, and transparency.

Fundamental principles of machine learning on Azure (15–20%)
Basic ML terminology (features, labels, training, evaluation).
Core ML types: supervised, unsupervised, and reinforcement learning.
High-level understanding of using Azure Machine Learning for model lifecycle.

Computer vision workloads on Azure (15–20%)
Image classification, object detection, optical character recognition, facial analysis.
Awareness of services like Azure AI Vision (formerly part of Cognitive Services).

Natural Language Processing (NLP) workloads on Azure (15–20%)
Key phrase extraction, sentiment analysis, language detection, translation, Q&A.
High-level use of services such as Azure AI Language.

Generative AI workloads on Azure (20–25%)
Concepts of large language models and generative AI.
How Azure OpenAI Service fits into Azure’s AI portfolio (prompting, embeddings, content considerations).

Target audience and prerequisites
AI-900 is intended for people starting in AI solution development, business decision-makers, or anyone needing to understand Azure-based AI capabilities. No prior data science or software engineering experience is required, but:

Basic understanding of cloud concepts and client–server applications is recommended.

Familiarity with general AI and ML concepts helps but is not mandatory.

The certification can serve as a stepping stone toward role-based certifications like Azure AI Engineer Associate or Azure Data Scientist Associate, though it is not a strict prerequisite.

Certkingdom.com offers the latest AI-901 exam dumps, practice questions, and verified answers to help you pass Microsoft Azure AI Fundamentals on your first attempt.

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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?

A. auto
B. none
C. required

Answer: C

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.

Question: 2
HOTSPOT
Select the answer that correctly completes the sentence.
Answer:
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: 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.
Answer:
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.
Questions and Answers PDF 4/48
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.

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
Answer: A, B
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: 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?

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

Answer: C

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.

Why Choose AI-901 Certification?

Beginner-friendly AI certification
No coding experience required
High demand in cloud and AI careers
Foundation for advanced Azure certifications
Recognized globally by employers

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What Students Commonly Ask ChatGPT About AI-901

Here are the most frequent queries students ask:
Is AI-901 easy to pass for beginners?
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What is the exam format and duration?
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Are practice tests enough to pass?

Certkingdom.com offers the latest AI-901 exam dumps, practice questions, and verified answers to help you pass Microsoft Azure AI Fundamentals on your first attempt.

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Certkingdom.com offers the latest AI-901 exam dumps, pr10 Most Asked FAQs About AI-901 Exam

1. What is AI-901 exam?
It is a beginner-level certification that tests knowledge of AI concepts and Azure AI services.

2. Is AI-901 difficult?
No, it is designed for beginners with basic understanding.

3. Do I need coding skills?
No coding is required for this exam.

4. What is the passing score?
Typically, 700 out of 1000.

5. How many questions are there?
Around 40–60 questions.

6. What is the exam duration?
Approximately 60 minutes.

7. Can I pass using dumps?
Practice tests can help, but understanding concepts is important.

8. Is AI-901 worth it?
Yes, it’s a great entry point into AI and cloud careers.

9. How long should I study?
1–3 weeks depending on your background.

10. What comes after AI-901?
You can move to advanced certifications like Azure AI Engineer Associate.actice questions, and verified answers to help you pass Microsoft Azure AI Fundamentals on your first attempt.

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