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Microsoft AI-901 Exam Questions

Exam Name: Microsoft Azure AI Fundamentals (Updated Version) Exam
Exam Code: AI-901
Related Certification(s): Microsoft Azure Certification
Certification Provider: Microsoft
Number of AI-901 practice questions in our database: 125 (updated: Oct. 23, 2026)
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Elizabeth Hernandez

1 month ago
Time management mattered because several questions were wordy, so I flagged anything uncertain and came back after clearing the easy ones. With that strategy and a final review of key AI capabilities, I passed the updated AI 901 exam.
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Patricia Flores

2 months ago
Responsible AI came as brief case studies requiring you to pick mitigation strategies for bias, privacy, or explainability requirements. Study common bias mitigation techniques, interpretability tools, and governance options to confidently answer those scenario-based questions.
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Harold Gonzalez

2 months ago
When the exam covers monitoring and troubleshooting in Foundry it gives incident-style questions about performance regressions, alerting thresholds, and rollback procedures. Study telemetry, logging, and incident response workflows so you can recommend concrete remediation steps, my friend passed after practicing real-world troubleshooting.
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Rebecca Green

21 days ago
Selected Answer: **A** Telemetry and logging are the foundation of incident response in Azure AI Foundry. When a performance regression hits, you need observable data before you can even begin scoping a rollback or adjusting alerting thresholds. Picking anything else here skips the diagnostic step entirely, which is exactly the kind of trap Microsoft sets in scenario-based questions on this exam.
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George Nelson

2 months ago
The Foundry related items were new to me, so I did a hands on walk through to understand the workflow and terminology instead of just memorizing definitions. Once I could picture the steps, the questions clicked and I passed AI 901 without surprises.
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Paul Garcia

3 months ago
Natural language questions often asked whether to use prebuilt text analytics, a chat model, or fine-tuning for domain-specific responses, usually in a choose-the-best-service format. Practice prompt scenarios, understand token limits, and learn the tradeoffs between fine-tuning and instruction-based approaches.
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Amanda Turner

3 months ago
Foundry responsible AI scenarios typically ask how to detect and mitigate bias or how to explain model decisions to stakeholders. Learn evaluation metrics for fairness, explainability tools, and how Foundry surfaces interpretability reports, someone I coached passed by concentrating on those topics.
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Frank Johnson

3 months ago
I found the exam less about math and more about interpreting requirements, like latency, privacy, and responsible AI, so I practiced reading prompts slowly and eliminating distractors. That approach helped me stay calm and I passed the Microsoft Certified Azure AI Fundamentals updated exam.
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Angela Gonzalez

4 months ago
Image classification versus object detection showed up as picture-based use cases where you must select the right service or model type for a camera feed. Focus on output differences, bounding box concepts, and how preprocessing like resizing or normalization affects model results.
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Monica Evans

4 months ago
Questions about implementing AI solutions by using Microsoft Foundry for model deployment will test choosing the right hosting option, configuring endpoints, and version control for models. Review deployment steps, scaling options, and canary or A/B strategies so you can justify choices under constraints, a teammate of mine passed after focusing on these areas.
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Lisa Mitchell

4 months ago
What tripped me up was choosing the right Azure AI service for a scenario, especially when Foundry options sounded similar, so I made a quick mapping sheet and it paid off on exam day. The case style questions felt familiar and I managed to pass on my first attempt.
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Melissa Martinez

5 months ago
I ran through precision and recall calculations on practice problems and saw several exam items asking which metric matters most for false positives versus false negatives in risk scenarios. A friend who passed the exam thanked Pass4Success for a compact set of questions that let them prepare quickly before the test.
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Nancy Jones

5 months ago
Foundry data ingestion questions often present a failing pipeline and ask which connector, format conversion, or schema mapping fixes it. Practice tracing Foundry ingestion flows, common file formats, and incremental load patterns, I passed the test and thanks Pass4Success for providing good collection of exam questions for preparation in short time.
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William Torres

6 months ago
The updated AI 901 leaned heavily on core AI concepts, so I spent time nailing the differences between machine learning, computer vision, and NLP and that made the questions feel straightforward. I passed after focusing on the official Microsoft Learn modules and reviewing the service capabilities the night before.
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Edward Brown

6 months ago
Choosing supervised versus unsupervised methods was often tested with short scenarios asking which approach fits a business goal, like churn prediction versus customer segmentation. Review common algorithms, required labels, and examples where unsupervised feature discovery is more appropriate.
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Karen Collins

6 months ago
When Identify AI concepts and capabilities shows up it usually uses short scenarios where you must pick which AI capability fits best, like image classification versus anomaly detection. Study the core definitions and typical business use cases so you can quickly match scenarios, I passed the exam and a colleague I mentored passed as well.
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Free Microsoft AI-901 Exam Actual Questions

Note: Premium Questions for AI-901 were last updated On Oct. 23, 2026 (see below)

Question #1

Capturing text from images is an example of which type of Al capability?

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Correct Answer: C

Question #2

You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.

Which type of natural language processing was performed?

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Correct Answer: A

The output identifies text values and labels them with entity categories, such as:

now [DateTime] students [PersonType] Microsoft [Organization] 175 [Quantity-Number] Today [DateTime-Date]

This is entity recognition, also known as Named Entity Recognition (NER). NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.

Why the other options are incorrect:


Question #3

Your company wants to build a recycling machine for bottles. The recycling machine must use cameras to automatically identify bottles of the correct shape and reject all other items.

Which type of Al workload should the company use?

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Correct Answer: D

A recycling machine that uses cameras to identify bottles of the correct shape and reject other items requires analysis of visual input. This is a computer vision workload.

Computer vision is used for image and video analysis tasks such as object detection, classification, shape recognition, and visual inspection.

The other options are incorrect:


Question #4

You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.

You need to perform speech-to-text transcription from the microphone.

Which Python class should you use?

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Correct Answer: C

Question #5

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?

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Correct Answer: C

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.


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