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Microsoft AI-901 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Microsoft Azure AI Fundamentals
Exam Number:	AI-901
Exam Format:	Multiple select, Multiple choice, Case studies
Available Languages:	Spanish, German, Indonesian (Indonesia), Italian, English, Chinese (Simplified), Japanese, Chinese (Traditional), Korean, Arabic (Saudi Arabia), Portuguese (Brazil), Russian, French
Passing Score:	700
Exam Price:	$99 USD
Certificate Validity Period:	Lifetime
Real Exam Qty:	30-40
Exam Duration:	60 minutes
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:

Topic	Details
Topic 1	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.
Topic 2	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.

Microsoft Azure AI Fundamentals Sample Questions (Q39-Q44):

NEW QUESTION # 39
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 40
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?

A. data zone standard
B. global batch
C. standard
D. provisioned

Answer: D

Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md

NEW QUESTION # 41
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools You submit a PDF for analysts.
What should the application do to retrieve the results?

A. Repeatedly call begin_analyze() with the same inputs to force a synchronous response.
B. Poll the operation results until the asynchronous analysis job succeeds
C. Send a GET request to the Microsoft Foundry endpoint to download the analyzer schema.
D. Use optical character recognition (OCR) to parse the text from the PDF and map the fields in the code.

Answer: B

Explanation:
Azure Content Understanding analysis operations are asynchronous long-running operations. Microsoft's Python SDK documentation states that analysis starts with a begin operation, then the application polls for results until analysis completes.
Therefore, after submitting the PDF for analysis, the application should poll the operation results until the asynchronous analysis job succeeds .

NEW QUESTION # 42
You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?

A. Call begin_analyze(), and then call poller.result() to retrieve the results.
B. Call analyze() to return the extracted fields synchronously in the same request.
C. Use optical character recognition (OCR) to extract text from the PDF and map the fields in the code.
D. Submit the PDF to an analyzer and read the results from the request headers.

Answer: A

Explanation:
To build an application that extracts fields from PDFs using Azure Content Understanding in Foundry Tools, you need to initialize the client library, use begin_analyze_binary to submit the file stream, and poll the long-running operation for the result.
Detail from implemenation:
Wait for the operation to complete and poll the results
result = poller.result()
Reference:
https://lobehub.com/pt-BR/skills/frank-luongt-faos-skills-marketplace-azure-ai-contentunderstanding-py

NEW QUESTION # 43
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.
Statement 1: System prompts can be used to authorize users. = No
Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.
Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.
Statement 3: A system prompt guides the behavior of a generative AI model. = Yes This is correct. Microsoft documentation says system messages help steer the model's behavior, tone, and output format.

NEW QUESTION # 44
......

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