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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Certificate Validity Period:Does not expire (Fundamentals certification)
Exam Price:Approx. 99 USD (varies by region)
Real Exam Qty:40-60
Available Languages:Korean, Portuguese (Brazil), Spanish, Japanese, French, English, Chinese (Traditional), Chinese (Simplified), German
Exam Duration:60 minutes
Exam Format:Case study (limited), Multiple response, Multiple choice, Drag and drop
Passing Score:700 (out of 1000)
Related Certifications:Microsoft Azure Data Fundamentals (DP-900)
Microsoft Azure Fundamentals (AZ-900)
Recommended Training:Azure AI Fundamentals Course
Microsoft Learn - AI-900 Learning Path
Exam Registration:Microsoft Certification Portal
Schedule exam via Pearson VUE
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

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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 (Q94-Q99):

NEW QUESTION # 94
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?

Answer: A

Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.


NEW QUESTION # 95
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools.
You submit a PDF for analysis.
What should the application do to retrieve the results?

Answer: C

Explanation:
To retrieve the extraction results using the official azure-ai-contentunderstanding Python SDK, the application must call begin_analyze(), and then call poller.result() to retrieve the results.
Because document analysis is an asynchronous operation, the process is split into initiating the request and waiting for the long-running operation to complete.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api


NEW QUESTION # 96
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?

Answer: C

Explanation:
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:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.


NEW QUESTION # 97
What should you use to compare benchmark metrics of different AI models?

Answer: C

Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog


NEW QUESTION # 98
You have a Microsoft Foundry project that contains a vision-enabled chat model deployment.
You are developing a Python application that sends a request containing a user prompt and a local JPEG image.
You need to include the image as binary data in the request.
To what should you set the image_url.ur1 field?

Answer: A

Explanation:
To pass a local JPEG image as binary data to a vision-enabled chat model deployment in a Microsoft Foundry project, you must set the url field inside the image_url object to a Base64- encoded Data URI string.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/gpt-with-vision


NEW QUESTION # 99
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