AI-901 Practice Tests: What Changed in the Microsoft Azure AI Fundamentals Exam?

Updated AI-901 practice tests are available with 125 questions and answers. Practice with the AI-901 PDF questions and testing engine software, you can prepare for your Microsoft Azure AI Fundamentals exam well.

AI-900 vs. AI-901: What Changed in the Microsoft Azure AI Fundamentals Exam?

Since AI-900 exam, towarding the Microsoft Certified: Azure AI Fundamentals certification, retired on June 30, 2026, you need to complete the AI-901 exam. Compared with AI-900 exam, what changed in the Microsoft Azure AI Fundamentals (Updated Version) AI-901 exam?

Comparison	AI-900	AI-901
Current status	Retired on June 30, 2026	Active exam with no announced retirement date
Main emphasis	Understanding and describing AI concepts	Implementing AI solutions with Microsoft Foundry
Candidate background	Suitable for technical and nontechnical candidates; data science and software development experience was not required	Expects basic Python knowledge, familiarity with Azure resources, and foundational technical skills
Exam structure	Five separate domains covering AI workloads, machine learning, computer vision, NLP, and generative AI	Two domains: AI concepts and capabilities at 40 to 45%, and Foundry implementation at 55 to 60%
Generative AI	Identify generative AI concepts and Azure services	Select and deploy models, write prompts, use the Foundry SDK, and build lightweight applications
Agentic AI	Not listed as a distinct objective	Explicitly covers agentic AI and building single-agent solutions
Multimodal AI	Mainly identifies vision, speech, and NLP capabilities	Uses multimodal models for text, speech, images, and visual prompts
Information extraction	Includes document processing as a conceptual workload	Uses Content Understanding to extract information from documents, images, audio, and video

The clearest difference is the language used in the objectives. AI-900 asked candidates to “describe” and “identify.” AI-901 repeatedly uses “implement,” “create,” “deploy,” “build,” and “interact.” More than half of AI-901 now focuses on practical work in Microsoft Foundry, including prompts, model deployment, agents, SDK-based applications, multimodal models, and Content Understanding.

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The most updated AI-901 practice tests from DumpsBase will help you prepare more efficiently. These practice test questions are designed to give you additional opportunities to review exam-related knowledge and work through question-based revision. They can be used after knowing the differences between the AI-900 and AI-901, and completing the learning of the AI-901 exam objectives.

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Try 5 Free Demo Questions

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 Foundry Agent Service uses tool_choice to control tool calling. auto allows the model to decide whether to call a tool, while none prevents tool calls. Setting it to required forces the agent to call one or more tools.

Question 2

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: Azure Content Understanding analysis operations are long-running operations in the Python SDK. begin_analyze() submits the content and returns a poller. Calling poller.result() waits for the operation and retrieves the results. The results are not returned in request headers or synchronously through analyze().

Question 3

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
Answer: B
Explanation: System instructions define an agent’s goals, constraints, role, and behavior. TPM controls throughput, temperature affects response variability, and max completion tokens limits response length.

Question 4

Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
A. sentiment analysis
B. Named Entity Recognition (NER)
C. summarization
D. key phrase extraction
Answer: B
Explanation: Named Entity Recognition identifies and categorizes entities in unstructured text, including people, organizations, and locations. Sentiment analysis detects opinion, summarization shortens text, and key phrase extraction identifies important phrases without classifying them as those entity types.

Question 5

You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?
A. client.recognize_linked_entities()
B. client.recognize_entities()
C. client.begin_extract_summary()
D. client.extract_key_phrases()
Answer: D

Explanation: client.extract_key_phrases() returns key phrases or main topics from unstructured text. The linked-entity method connects detected entities to a knowledge base, the entity-recognition method identifies named entities, and the summary method starts extractive summarization.

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