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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-901
Available Languages:Italian, German, Spanish, Chinese (Traditional), Japanese, Portuguese (Brazil), Korean, French, English, Chinese (Simplified), Indonesian (Indonesia), Arabic (Saudi Arabia), Russian
Exam Format:Case studies, Multiple select, Multiple choice
Exam Price:$99 USD
Exam Duration:60 minutes
Passing Score:700
Real Exam Qty:30-40
Certificate Validity Period:Lifetime
Recommended Training:Microsoft Learn: Get started with Microsoft Foundry
Microsoft Learn: AI concepts for developers and technology professionals
Exam Registration:Microsoft Certification Exam Registration
Pearson VUE Scheduling
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.
Topic 2
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.

Microsoft Azure AI Fundamentals Sample Questions (Q34-Q39):

NEW QUESTION # 34
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?

Answer: A

Explanation:
For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription .
Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage , including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.
Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio.
Speech translation translates speech between languages, but the requirement is to generate transcripts.


NEW QUESTION # 35
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?

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.


NEW QUESTION # 36
Which feature of the Azure Language in Foundry Tools service should you use to automate the masking of names and phone numbers in text data?

Answer: C

Explanation:
To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.
PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.
The other options are incorrect:
A). custom text classification categorizes documents or text into labels.
C). entity linking identifies entities and links them to knowledge base entries.
D). custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.


NEW QUESTION # 37
Drag and Drop Question
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:


NEW QUESTION # 38
Hotspot Question
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
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:

Explanation:

Explanation:
Box: analyzer_id
Core Method Signature
poller = client.begin_analyze(
analyzer_id="YOUR_ANALYZER_ID",
inputs=[AnalysisInput(url="FILE_URL")],
**kwargs
)
Note:
client.begin_analyze is the primary method used in the official Azure AI Content Understanding Python SDK (azure-ai-contentunderstanding) to kick off asynchronous, long-running operations (LRO) for extracting structured data and layout information from multimodal files.
Formerly a part of standalone Azure AI services, Content Understanding is fully integrated into the Microsoft Foundry ecosystem. This service utilizes foundational large language models deployed inside your Foundry resource to analyze documents, images, audio, and video streams based on prebuilt or custom schemas.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api


NEW QUESTION # 39
......

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