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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 45-60 |
| Exam Price: | USD 99 |
| Available Languages: | English, Portuguese (Brazil), French, Japanese, Korean, Italian, Chinese (Traditional), Indonesian (Indonesia), German, Spanish, Arabic (Saudi Arabia), Russian, Chinese (Simplified) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Format: | Multiple choice, Scenario-based, Drag and drop, Multiple select, Match the service to use case |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
>> Valid AI-901 Exam Duration <<
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NEW QUESTION # 115
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 # 116
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: a multimodal
Processing prompts that contain images requires you to deploy and use a multimodal model (such as GPT-4o) via Microsoft Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/flow-process-image
NEW QUESTION # 117
You are developing an AI-powered customer support application. Which task is an example of the Microsoft responsible AI principle of inclusiveness?
Answer: D
Explanation:
The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.
Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.
NEW QUESTION # 118
You have an Azure subscription that uses Azure OpenAI.
You need to create an original image of a rural scene to use on a website.
What should you do?
Answer: A
Explanation:
Azure OpenAI in Azure AI Foundry Models
Image generation models
The image generation models generate images from text prompts that the user provides. GPT- image-1 is in limited access public preview. DALL-E 3 is generally available for use with the REST APIs. DALL-E 2 and DALL-E 3 with client SDKs are in preview.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
NEW QUESTION # 119
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 # 120
......
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