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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Available Languages: | French, Korean, Chinese (Simplified), Chinese (Traditional), English, Spanish, Indonesian (Indonesia), Japanese, Arabic (Saudi Arabia), Italian, Russian, Portuguese (Brazil), German |
| Passing Score: | 700 / 1000 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Exam Duration: | 45-60 |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 99 |
| Exam Format: | Drag and drop, Match the service to use case, Scenario-based, Multiple select, Multiple choice |
| 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 |
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NEW QUESTION # 129
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: API endpoint
An API endpoint URL is what applications call to send requests to a deployed generative AI model.
Key Components of Deployed Model Requests
API Endpoint: The specific web address hosted by a cloud provider or server.
HTTP Method: Typically a POST request used to send data to the model.
Authentication: A secure API key passed in the request header to verify identity.
Payload: A JSON body containing the prompt, token limits, and temperature settings.
Reference:
https://help.sap.com/docs/sap-ai-core/generative-ai/consume-models
NEW QUESTION # 130
You plan to deploy a generative AI model to a Microsoft Foundry project in the East US Azure region. What should you check first?
Answer: C
Explanation:
Model availability should be checked first. You must confirm that the specific generative AI model is supported and offered in the East US region before checking your token rate limits or allocation quota, as a model cannot be deployed in a region where it does not exist.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/quotas-limits
NEW QUESTION # 131
You plan to create an AI application by using Azure AI Foundry. The solution will be deployed to dedicated virtual machines. Which deployment option should you use?
Answer: A
Explanation:
Deployment options
Azure AI Foundry provides several deployment options depending on the type of models and resources you need to provision. The following deployment options are available:
* Standard deployment in Azure AI Foundry resources
* Deployment to serverless API endpoints
* Deployment to managed computes
Serverless API endpoint
This deployment option is available only in Azure AI hub resources. It allows you to create dedicated endpoints to host the model, accessible through an API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing, and you can create only regional deployments for serverless API endpoints.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview
NEW QUESTION # 132
Hotspot Question
Select the answer that correctly complete the sentence.
Answer:
Explanation:
NEW QUESTION # 133
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
* Fairness
* Privacy and security
* Transparency
* Reliability and safety
Comprehensive and Detailed Explanation with all Azure AI documents : =
Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
Fairness
Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.
NEW QUESTION # 134
......
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